{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-13T18:04:26.406407Z",
     "start_time": "2019-05-13T18:04:26.395100Z"
    }
   },
   "source": [
    "## Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:53:37.956717Z",
     "start_time": "2019-10-21T12:53:37.892752Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Appended C:\\Users\\black\\PycharmProjects\\BatchBALD\\src to paths\n",
      "Switched to directory C:\\Users\\black\\PycharmProjects\\BatchBALD\n",
      "%load_ext autoreload\n",
      "%autoreload 2\n"
     ]
    }
   ],
   "source": [
    "import blackhc.notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:53:38.531018Z",
     "start_time": "2019-10-21T12:53:38.079352Z"
    }
   },
   "outputs": [],
   "source": [
    "import al_notebook.results_loader as rl\n",
    "import al_notebook.plots as alp\n",
    "from acquisition_functions import AcquisitionFunction\n",
    "from acquisition_method import AcquisitionMethod\n",
    "from dataset_enum import DatasetEnum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:53:38.540348Z",
     "start_time": "2019-10-21T12:53:38.532205Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:53:38.580866Z",
     "start_time": "2019-10-21T12:53:38.541240Z"
    }
   },
   "outputs": [],
   "source": [
    "import prettyprinter as pp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:53:38.614526Z",
     "start_time": "2019-10-21T12:53:38.606461Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:53:38.985270Z",
     "start_time": "2019-10-21T12:53:38.953539Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:53:41.009732Z",
     "start_time": "2019-10-21T12:53:40.988727Z"
    }
   },
   "outputs": [],
   "source": [
    "#MARKERS = (\"X\", \"s\", \"o\")\n",
    "MARKERS = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Common functions\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:53:41.566891Z",
     "start_time": "2019-10-21T12:53:41.541518Z"
    }
   },
   "outputs": [],
   "source": [
    "def acc_label_axes():\n",
    "    plt.xlabel('Acquired dataset size')\n",
    "    plt.ylabel('Accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RMNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:53:45.732884Z",
     "start_time": "2019-10-21T12:53:44.749680Z"
    }
   },
   "outputs": [],
   "source": [
    "stores_rmnist = rl.load_experiment_results('paper')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:53:45.744184Z",
     "start_time": "2019-10-21T12:53:45.734060Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stores_rmnist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-22T17:57:44.506738Z",
     "start_time": "2019-05-22T17:57:44.493892Z"
    }
   },
   "source": [
    "### RMNIST ablation study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:54:15.145003Z",
     "start_time": "2019-10-21T12:54:14.791045Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    'acquisition_method': {\n",
      "        acquisition_method.AcquisitionMethod.multibald,\n",
      "        acquisition_method.AcquisitionMethod.independent\n",
      "    },\n",
      "    'experiment_description': {\n",
      "        'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD and '\n",
      "            'heuristic',\n",
      "        'Additional paper experiments (#1): RMNIST w/ noise, MNIST w/ noise, '\n",
      "            'MNIST, with initial samples'\n",
      "    },\n",
      "    'dataset': {\n",
      "        dataset_enum.DatasetEnum.repeated_mnist_w_noise5,\n",
      "        dataset_enum.DatasetEnum.repeated_mnist_w_noise,\n",
      "        dataset_enum.DatasetEnum.repeated_mnist_w_noise2,\n",
      "        dataset_enum.DatasetEnum.mnist_w_noise\n",
      "    },\n",
      "    'experiments_laaos': {\n",
      "        './experiment_configs/rmnist_w_noise/configs.py',\n",
      "        'experiment_configs/rmnist_w_noise_2_5/configs.py',\n",
      "        './experiment_configs/paper_exp1/configs.py'\n",
      "    },\n",
      "    'num_acquired_points': {320, 300},\n",
      "    'balanced_validation_set': {False, None},\n",
      "    'balanced_test_set': {False, None}\n",
      "}\n",
      "{\n",
      "    'BALD (0 reps)': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'Additional paper experiments (#1): RMNIST w/ noise, MNIST w/ '\n",
      "            'noise, MNIST, with initial samples'\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'dataset': {dataset_enum.DatasetEnum.mnist_w_noise},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'experiments_laaos': {'./experiment_configs/paper_exp1/configs.py'},\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {320},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BALD (1 reps)': {\n",
      "        'num_trials': 6,\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise2},\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD '\n",
      "            'and heuristic'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'experiments_laaos': {\n",
      "            'experiment_configs/rmnist_w_noise_2_5/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'balanced_validation_set': {False},\n",
      "        'balanced_test_set': {False},\n",
      "        'num_acquired_points': {320},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BALD (4 reps)': {\n",
      "        'num_trials': 6,\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise5},\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD '\n",
      "            'and heuristic'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'experiments_laaos': {\n",
      "            'experiment_configs/rmnist_w_noise_2_5/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'balanced_validation_set': {False},\n",
      "        'balanced_test_set': {False},\n",
      "        'num_acquired_points': {320},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BatchBALD (4 reps)': {\n",
      "        'num_trials': 6,\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise5},\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {acquisition_method.AcquisitionMethod.multibald},\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD '\n",
      "            'and heuristic'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'experiments_laaos': {\n",
      "            'experiment_configs/rmnist_w_noise_2_5/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'balanced_validation_set': {False},\n",
      "        'balanced_test_set': {False},\n",
      "        'num_acquired_points': {320},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BALD (2 reps)': {\n",
      "        'num_trials': 6,\n",
      "        'num_inference_samples': {10},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'RMNIST with noise k10 b5 and b10 (and k100 b10), BALD, BatchBALD '\n",
      "            'and heuristic'\n",
      "        },\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                38043,\n",
      "                40091,\n",
      "                17418,\n",
      "                2094,\n",
      "                39879,\n",
      "                3133,\n",
      "                5011,\n",
      "                40683,\n",
      "                54379,\n",
      "                24287,\n",
      "                9849,\n",
      "                59305,\n",
      "                39508,\n",
      "                39356,\n",
      "                8758,\n",
      "                52579,\n",
      "                13655,\n",
      "                7636,\n",
      "                21562,\n",
      "                41329\n",
      "            )\n",
      "        },\n",
      "        'initial_percentage': {100},\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'dataset': {dataset_enum.DatasetEnum.repeated_mnist_w_noise},\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/rmnist_w_noise/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'initial_samples_per_class': {2},\n",
      "        'num_acquired_points': {300},\n",
      "        'num_initial_samples': {20},\n",
      "        'tag': {'paper'}\n",
      "    }\n",
      "}\n",
      "BatchBALD (4 reps):\n",
      "85% at [50 60 70]\n",
      "90% at [ 90 100 110]\n",
      "95% at [250 260 270]\n",
      "BALD (0 reps):\n",
      "85% at [ 70.  90. 100.]\n",
      "90% at [120. 120. 160.]\n",
      "95% at [250. 270. 290.]\n",
      "BALD (1 reps):\n",
      "85% at [ 80. 100. 130.]\n",
      "90% at [160. 160. 190.]\n",
      "95% at [inf inf inf]\n",
      "BALD (2 reps):\n",
      "85% at [140. 170. 180.]\n",
      "90% at [260. 280.  inf]\n",
      "95% at [inf inf inf]\n",
      "BALD (4 reps):\n",
      "85% at [130. 160. 200.]\n",
      "90% at [240. 300.  inf]\n",
      "95% at [inf inf inf]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for show_b in (10, ):\n",
    "    filtered = {}\n",
    "\n",
    "    def filter_exps(name, store):\n",
    "        vip_args = rl.get_vip_args(store)\n",
    "\n",
    "        if vip_args.ds not in (DatasetEnum.repeated_mnist_w_noise,\n",
    "                               DatasetEnum.mnist_w_noise,\n",
    "                               DatasetEnum.repeated_mnist_w_noise2,\n",
    "                               DatasetEnum.repeated_mnist_w_noise5):\n",
    "            return False\n",
    "\n",
    "        if vip_args.nis == 0:\n",
    "            return False\n",
    "\n",
    "    #     if vip_args.nap < 300:\n",
    "    #         return False\n",
    "\n",
    "        if (vip_args.am, vip_args.af) not in ((AcquisitionMethod.multibald,\n",
    "                                               AcquisitionFunction.bald),\n",
    "                                              (AcquisitionMethod.independent,\n",
    "                                               AcquisitionFunction.bald),\n",
    "                                              #(AcquisitionMethod.independent,\n",
    "                                              # AcquisitionFunction.random)\n",
    "                                             ):\n",
    "            return False\n",
    "        \n",
    "        \n",
    "        if vip_args.am == AcquisitionMethod.multibald:\n",
    "            if vip_args.ds != DatasetEnum.repeated_mnist_w_noise5:\n",
    "                return False\n",
    "\n",
    "        if vip_args.af == AcquisitionFunction.bald:\n",
    "            if vip_args.k not in (10, ):\n",
    "                return False\n",
    "\n",
    "            if vip_args.b not in (show_b, ):\n",
    "                return False\n",
    "        return True\n",
    "\n",
    "    filtered.update(rl.filter_dict(stores_rmnist, kv=filter_exps))\n",
    "\n",
    "    pp.pprint(rl.diff_args(filtered))\n",
    "\n",
    "    def key2text(name, store):\n",
    "\n",
    "        vip_args = rl.get_vip_args(store)\n",
    "        am, af = vip_args.am, vip_args.af\n",
    "        ds = vip_args.ds\n",
    "        num_repetitions = {\n",
    "            DatasetEnum.mnist_w_noise: 0,\n",
    "            DatasetEnum.repeated_mnist_w_noise: 2,\n",
    "            DatasetEnum.repeated_mnist_w_noise2: 1,\n",
    "            DatasetEnum.repeated_mnist_w_noise5: 4\n",
    "        }[ds]\n",
    "\n",
    "        if (am, af) == (AcquisitionMethod.independent,\n",
    "                        AcquisitionFunction.random):\n",
    "            return 'Random (all)'\n",
    "        elif (am, af) == (AcquisitionMethod.independent,\n",
    "                          AcquisitionFunction.bald):\n",
    "            return f'BALD ({num_repetitions} reps)'\n",
    "        elif (am, af) == (AcquisitionMethod.multibald,\n",
    "                          AcquisitionFunction.bald):\n",
    "            return f'BatchBALD ({num_repetitions} reps)'\n",
    "        else:\n",
    "            raise ValueError(vip_args)\n",
    "        \n",
    "    #grouped_by = rl.groupby_dict(filtered, key_kv=rl.get_diff_args_key2text(filtered, ('tag',)))\n",
    "    grouped_by = rl.groupby_dict(filtered, key_kv=key2text)\n",
    "\n",
    "    pp.pprint(\n",
    "        rl.map_dict(grouped_by, v=lambda stores: rl.get_stores_info(stores)))\n",
    "\n",
    "    grouped_by = rl.map_dict(\n",
    "        grouped_by,\n",
    "        v=lambda stores: rl.aggregate_accuracies(stores, thresholds=(85, 90, 95)))\n",
    "\n",
    "    sorted_dict = rl.sort_dict(grouped_by, v=rl.get_threshold_quantiles_key)\n",
    "\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    #plt.title(\"RMNIST\")\n",
    "    alp.plot_aggregated_groups(sorted_dict,\n",
    "                               show_num_trials=False,\n",
    "                               show_quantiles=False,\n",
    "                               show_thresholds=False)\n",
    "    plt.axis([50, 280, 0.7, 0.96])\n",
    "    acc_label_axes()\n",
    "    plt.grid(True)\n",
    "    plt.legend(loc='lower right', fontsize=9)\n",
    "    plt.title(\"RMNIST repetition exp\")\n",
    "    plt.tight_layout()\n",
    "\n",
    "    output_path = blackhc.notebook.original_dir + '/RMNIST_ablation_poster.pdf'\n",
    "    alp.plot_save(output_path)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CINIC10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:54:20.518080Z",
     "start_time": "2019-10-21T12:54:20.044349Z"
    }
   },
   "outputs": [],
   "source": [
    "stores_cinic10 = stores_rmnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:54:21.650508Z",
     "start_time": "2019-10-21T12:54:21.622068Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "159"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(stores_cinic10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-05-22T17:57:44.506738Z",
     "start_time": "2019-05-22T17:57:44.493892Z"
    }
   },
   "source": [
    "### CINIC10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-10-21T12:55:36.153237Z",
     "start_time": "2019-10-21T12:55:35.763839Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    'acquisition_method': {\n",
      "        acquisition_method.AcquisitionMethod.multibald,\n",
      "        acquisition_method.AcquisitionMethod.independent\n",
      "    },\n",
      "    'initial_samples': {\n",
      "        (\n",
      "            175625,\n",
      "            83927,\n",
      "            82616,\n",
      "            85656,\n",
      "            85511,\n",
      "            173978,\n",
      "            178296,\n",
      "            84839,\n",
      "            171094,\n",
      "            85922,\n",
      "            82234,\n",
      "            171293,\n",
      "            173785,\n",
      "            83635,\n",
      "            178024,\n",
      "            171598,\n",
      "            176883,\n",
      "            81731,\n",
      "            177912,\n",
      "            179940,\n",
      "            125042,\n",
      "            118699,\n",
      "            35438,\n",
      "            120390,\n",
      "            125906,\n",
      "            117728,\n",
      "            124301,\n",
      "            35687,\n",
      "            125646,\n",
      "            34313,\n",
      "            119522,\n",
      "            119199,\n",
      "            29000,\n",
      "            120874,\n",
      "            121386,\n",
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      "            27711,\n",
      "            118506,\n",
      "            34226,\n",
      "            119439,\n",
      "            18526,\n",
      "            20535,\n",
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      "            22101,\n",
      "            113495,\n",
      "            114290,\n",
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      "            22368,\n",
      "            113691,\n",
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      "            114803,\n",
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      "            131277,\n",
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      "            129682,\n",
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      "            133165,\n",
      "            41646,\n",
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      "            41709,\n",
      "            42278,\n",
      "            130956,\n",
      "            94090,\n",
      "            289,\n",
      "            8984,\n",
      "            294,\n",
      "            7860,\n",
      "            2497,\n",
      "            93574,\n",
      "            94555,\n",
      "            5274,\n",
      "            8762,\n",
      "            1800,\n",
      "            95439,\n",
      "            93545,\n",
      "            91021,\n",
      "            7900,\n",
      "            3773,\n",
      "            95172,\n",
      "            90486,\n",
      "            2,\n",
      "            92917,\n",
      "            104980,\n",
      "            12957,\n",
      "            12842,\n",
      "            106634,\n",
      "            99911,\n",
      "            14598,\n",
      "            16697,\n",
      "            107837,\n",
      "            10488,\n",
      "            15136,\n",
      "            15459,\n",
      "            14833,\n",
      "            104663,\n",
      "            14828,\n",
      "            107669,\n",
      "            102885,\n",
      "            101266,\n",
      "            100879,\n",
      "            15825,\n",
      "            99563,\n",
      "            150941,\n",
      "            61606,\n",
      "            145855,\n",
      "            148621,\n",
      "            55293,\n",
      "            149885,\n",
      "            147442,\n",
      "            146642,\n",
      "            149736,\n",
      "            55839,\n",
      "            150745,\n",
      "            144586,\n",
      "            56184,\n",
      "            59494,\n",
      "            149268,\n",
      "            148840,\n",
      "            145134,\n",
      "            59746,\n",
      "            148059,\n",
      "            150984\n",
      "        ),\n",
      "        None\n",
      "    },\n",
      "    'experiments_laaos': {\n",
      "        './experiment_configs/cinic10/configs.py',\n",
      "        'experiment_configs/cinic10/configs.py'\n",
      "    }\n",
      "}\n",
      "{\n",
      "    'BALD': {\n",
      "        'num_trials': 7,\n",
      "        'num_inference_samples': {50},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {\n",
      "            acquisition_method.AcquisitionMethod.independent\n",
      "        },\n",
      "        'experiment_description': {\n",
      "            'k50 on CIFAR10 and CINIC10 with 20 nispc (but not fixed)'\n",
      "        },\n",
      "        'dataset': {dataset_enum.DatasetEnum.cinic10},\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'initial_samples_per_class': {20},\n",
      "        'balanced_validation_set': {True},\n",
      "        'balanced_test_set': {True},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                175625,\n",
      "                83927,\n",
      "                82616,\n",
      "                85656,\n",
      "                85511,\n",
      "                173978,\n",
      "                178296,\n",
      "                84839,\n",
      "                171094,\n",
      "                85922,\n",
      "                82234,\n",
      "                171293,\n",
      "                173785,\n",
      "                83635,\n",
      "                178024,\n",
      "                171598,\n",
      "                176883,\n",
      "                81731,\n",
      "                177912,\n",
      "                179940,\n",
      "                125042,\n",
      "                118699,\n",
      "                35438,\n",
      "                120390,\n",
      "                125906,\n",
      "                117728,\n",
      "                124301,\n",
      "                35687,\n",
      "                125646,\n",
      "                34313,\n",
      "                119522,\n",
      "                119199,\n",
      "                29000,\n",
      "                120874,\n",
      "                121386,\n",
      "                28911,\n",
      "                27711,\n",
      "                118506,\n",
      "                34226,\n",
      "                119439,\n",
      "                18526,\n",
      "                20535,\n",
      "                113279,\n",
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      "                113495,\n",
      "                114290,\n",
      "                111722,\n",
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      "                22368,\n",
      "                113691,\n",
      "                111970,\n",
      "                25996,\n",
      "                20965,\n",
      "                114803,\n",
      "                115951,\n",
      "                109256,\n",
      "                24564,\n",
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      "                166861,\n",
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      "                78499,\n",
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      "                79423,\n",
      "                167614,\n",
      "                78538,\n",
      "                165327,\n",
      "                169993,\n",
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      "                157194,\n",
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      "                160910,\n",
      "                156573,\n",
      "                160350,\n",
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      "                159430,\n",
      "                52358,\n",
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      "                52870,\n",
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      "                48286,\n",
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      "                140017,\n",
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      "                126668,\n",
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      "                129682,\n",
      "                40734,\n",
      "                133165,\n",
      "                41646,\n",
      "                129465,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                38415,\n",
      "                134684,\n",
      "                42990,\n",
      "                41709,\n",
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      "                130956,\n",
      "                94090,\n",
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      "                8984,\n",
      "                294,\n",
      "                7860,\n",
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      "                93574,\n",
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      "                5274,\n",
      "                8762,\n",
      "                1800,\n",
      "                95439,\n",
      "                93545,\n",
      "                91021,\n",
      "                7900,\n",
      "                3773,\n",
      "                95172,\n",
      "                90486,\n",
      "                2,\n",
      "                92917,\n",
      "                104980,\n",
      "                12957,\n",
      "                12842,\n",
      "                106634,\n",
      "                99911,\n",
      "                14598,\n",
      "                16697,\n",
      "                107837,\n",
      "                10488,\n",
      "                15136,\n",
      "                15459,\n",
      "                14833,\n",
      "                104663,\n",
      "                14828,\n",
      "                107669,\n",
      "                102885,\n",
      "                101266,\n",
      "                100879,\n",
      "                15825,\n",
      "                99563,\n",
      "                150941,\n",
      "                61606,\n",
      "                145855,\n",
      "                148621,\n",
      "                55293,\n",
      "                149885,\n",
      "                147442,\n",
      "                146642,\n",
      "                149736,\n",
      "                55839,\n",
      "                150745,\n",
      "                144586,\n",
      "                56184,\n",
      "                59494,\n",
      "                149268,\n",
      "                148840,\n",
      "                145134,\n",
      "                59746,\n",
      "                148059,\n",
      "                150984\n",
      "            ),\n",
      "            None\n",
      "        },\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/cinic10/configs.py',\n",
      "            'experiment_configs/cinic10/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'num_acquired_points': {1400},\n",
      "        'num_initial_samples': {200},\n",
      "        'tag': {'paper'}\n",
      "    },\n",
      "    'BatchBALD': {\n",
      "        'num_trials': 7,\n",
      "        'num_inference_samples': {50},\n",
      "        'available_sample_k': {10},\n",
      "        'type': {acquisition_functions.AcquisitionFunction.bald},\n",
      "        'acquisition_method': {acquisition_method.AcquisitionMethod.multibald},\n",
      "        'experiment_description': {\n",
      "            'k50 on CIFAR10 and CINIC10 with 20 nispc (but not fixed)'\n",
      "        },\n",
      "        'dataset': {dataset_enum.DatasetEnum.cinic10},\n",
      "        'initial_percentage': {100},\n",
      "        'reduce_percentage': {0},\n",
      "        'min_remaining_percentage': {100},\n",
      "        'min_candidates_per_acquired_item': {100},\n",
      "        'initial_samples_per_class': {20},\n",
      "        'balanced_validation_set': {True},\n",
      "        'balanced_test_set': {True},\n",
      "        'initial_samples': {\n",
      "            (\n",
      "                175625,\n",
      "                83927,\n",
      "                82616,\n",
      "                85656,\n",
      "                85511,\n",
      "                173978,\n",
      "                178296,\n",
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      "                171094,\n",
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      "                113921,\n",
      "                110333,\n",
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      "                79423,\n",
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      "                66941,\n",
      "                71367,\n",
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      "                71914,\n",
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      "                71981,\n",
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      "                45961,\n",
      "                52870,\n",
      "                47973,\n",
      "                140114,\n",
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      "                53562,\n",
      "                48741,\n",
      "                135932,\n",
      "                139631,\n",
      "                142293,\n",
      "                139998,\n",
      "                47392,\n",
      "                49745,\n",
      "                48286,\n",
      "                47315,\n",
      "                49955,\n",
      "                45065,\n",
      "                139344,\n",
      "                140017,\n",
      "                43982,\n",
      "                126010,\n",
      "                127867,\n",
      "                126668,\n",
      "                130786,\n",
      "                131772,\n",
      "                131277,\n",
      "                133778,\n",
      "                132165,\n",
      "                129682,\n",
      "                40734,\n",
      "                133165,\n",
      "                41646,\n",
      "                129465,\n",
      "                38415,\n",
      "                134684,\n",
      "                42990,\n",
      "                41709,\n",
      "                42278,\n",
      "                130956,\n",
      "                94090,\n",
      "                289,\n",
      "                8984,\n",
      "                294,\n",
      "                7860,\n",
      "                2497,\n",
      "                93574,\n",
      "                94555,\n",
      "                5274,\n",
      "                8762,\n",
      "                1800,\n",
      "                95439,\n",
      "                93545,\n",
      "                91021,\n",
      "                7900,\n",
      "                3773,\n",
      "                95172,\n",
      "                90486,\n",
      "                2,\n",
      "                92917,\n",
      "                104980,\n",
      "                12957,\n",
      "                12842,\n",
      "                106634,\n",
      "                99911,\n",
      "                14598,\n",
      "                16697,\n",
      "                107837,\n",
      "                10488,\n",
      "                15136,\n",
      "                15459,\n",
      "                14833,\n",
      "                104663,\n",
      "                14828,\n",
      "                107669,\n",
      "                102885,\n",
      "                101266,\n",
      "                100879,\n",
      "                15825,\n",
      "                99563,\n",
      "                150941,\n",
      "                61606,\n",
      "                145855,\n",
      "                148621,\n",
      "                55293,\n",
      "                149885,\n",
      "                147442,\n",
      "                146642,\n",
      "                149736,\n",
      "                55839,\n",
      "                150745,\n",
      "                144586,\n",
      "                56184,\n",
      "                59494,\n",
      "                149268,\n",
      "                148840,\n",
      "                145134,\n",
      "                59746,\n",
      "                148059,\n",
      "                150984\n",
      "            ),\n",
      "            None\n",
      "        },\n",
      "        'experiments_laaos': {\n",
      "            './experiment_configs/cinic10/configs.py',\n",
      "            'experiment_configs/cinic10/configs.py'\n",
      "        },\n",
      "        'quickquick': {False},\n",
      "        'num_acquired_points': {1400},\n",
      "        'num_initial_samples': {200},\n",
      "        'tag': {'paper'}\n",
      "    }\n",
      "}\n",
      "BatchBALD:\n",
      "50% at [280 280 290]\n",
      "55% at [580 610 610]\n",
      "58% at [ 960  990 1000]\n",
      "59% at [1150 1190 1280]\n",
      "BALD:\n",
      "50% at [280. 290. 290.]\n",
      "55% at [610. 620. 630.]\n",
      "58% at [1090. 1100. 1120.]\n",
      "59% at [1260. 1330. 1340.]\n"
     ]
    },
    {
     "data": {
      "image/png": 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RXLueJDTQhjGjBFNG2ftyj4+lAriiKMpHUKCnFhkK4KnfjClnFqbMUwswMhJmZMt96Ew24iouGl8f3N9dy+gb9yCMVvzF5xNXlE7YM4TnwEt4m7Zhr7ho6jLDIfydBwh01xIa6iTi7EVGghhSS9hmiCPtzb+RUr+B4Dl3YFj1hdhmIs1bmdO+m70Lb6A9HKbApCN+yQ0YKy9BH/Sid6SdtPUvpWR3azXZATeJC65H6I1omdPY07ATo9Axy5GCUUpWE+aFsMYPwiEsegNC02Oaf8UJ5c3IncXLhzawP+BiZkoBI5v+igz5iV/0MYzppad0b98tFcAVRVE+ggI9tQR6GzDnVTK6/WFS1nxtfDlSGY3ga92DKb0UzZYwIZ9r/3r8bftACPxte7BNPxfNkcroWw+i2RJwLL6ByMFmzHkzcR94Cedbf48tpjJ79QndyGFnH96Gt/C37MLrHcVntJCakIWlaAFaYjZ1Pi8963/JBX0N6GeuIrThD0jPEMa13yK4/i4W6A3U51TwQMdhEg0m+gNeXOEgJXEJ3OZIxXCSz1833MORI/WsSs5Gy5kFgJZayG5hoMJoxKTTiAy2cxVBno7qeb6jhmsKK6csz6hpTMur5FDN6/S9cR9GRyp9867ilajkqkgIq944Zd73SgVwRVGUjxgZDuGt24z30CsY00uRIT+uPU8Tv/h6gkNdOLc/QmigBb0jnaTz7xwP4oEjjXhqXsWUX4W1ZCmewxtw7XsedBr6hAwcC69FZzCPX2d4wx8Z1Zvoa91DX/Mu/PZUcqzx5MYlEhzuZmj9z5GREPr0Up5KKaM14OHqrDIWZRQTGu5i/TM/wB6NMPO6n6HPqyT00s8JbfozkZadyP4WbB//PRdllrC+twW3EGRb7Jg0jd3DR3i0q44bcqZP+uw5FI3w+L71JMgoVQsuH0/jR1AjBbdGAgBEB5pZqBkoMCdyT8NOsm3xlDpSSDHZJi13bmEVB9v386zJTF/6NEZ7W+nze1iRXki+Pel9/x5VAFcURfmICQ62EexrAsDfupukC76It3ErMhLB37YXoemxzbwAb91Ghl77PUnnfw6hN+Lc9hCa2UHczPMRmgHHvMsJFS0k1N+EpWgR4phWpr+9mobuOp4tWIoMBxB71iFyZmPRDHx+xjLiDrwEQNL5d7LFOUTjK78nqbOah41W6ka7KfQN0Z07n5VnfwpDfhUAxtVfQdgSCa77MbqC+WjTz6VQCO4oip2XUtLsHUVIwc6hHuKDPs7q3IttxnkYUwvH6/Zy3Vv0jxzhhvKzMCZmjx/fN9RNGJgX8iC9I0QH2tBSC/hMdiXf2L2Oazb8HYAEo4XPli/hc9PPmnBfc2wJpBYvojnko9ieRGt/O4+2VnNzyTwVwBVFUZR/XrCnjvBwNxAL4Kbc2YT6GvA1bcOUMxtL6TI6f7UWx9KPE3EPMPTa79EnZBJ29RG/7JMI7e3OaUNi1qSbiYxsupvqxALMidmsOvAcdquV+HNu5aHWA9y9/1Vuaq8mrews+qKS5w6+SnHLNtYUzmFjchF7/T7qZARDxjQqSxZOKNew/FZ0eXPQJechhCAYjfBQx2G2DnWzY/gIQyE/s+zJfK6wkldrNmAizJwdj2KfdxWmjBKOOAfY0LCNGY4UCisvnFD2joF2AKp00NCym2TXAMnFi7i5ZB7nZZXQMDpAg3OAzb3N/HD/6wSjEb40c8V4fiEEN5fORwB/rNvKA817ubawkjU5p2c1NhXAFUVRPmL8XYcIjx7BlFtJoKMaf9O22LNr7wiGhExce54h1N/C6Ka/kP25x3HtepzwSDfWaWef0upnOu8gnQdeon32pcyz2ckzWqBtD7bBdq4prODeDffwZFTHZ/Pn8EjTPrSa1zlb02Fe+y1WawZShnt5faCNxUlZmI7bwxtAy58z/vq3TXv5dfMeci12zknNI9Fg4u62A/T01JIf8vJSdgVd3gGqdj5O+dxLebRuGzoZZdXia07YH3zXQCdljhQ0/DzZeRgbBq5LyCILyLbGk22NZ2VmMbeWLuArO5/jroObACYEcbOm5y912/nJgY1cmT+b71ZdcNrmg6sAriiKcgaLeIbx1G0h6nchgx4iAQ+W/HnYypdPmj4a8OBv2QXRMInnf46+B7+M5/AG4iouGh/E5tr9FEJvIjzSg695O47FNxDqa8RSvPiU6mSpe5b9cSlE7clUaTpERhmyfjPhvc+SvOxmVrmO8EJCHr9pPUhnzeus6dpH4sd/O94FvyApg3JHEnHayYahgTcc4v6OQ6xKy+cvc2KtaSklh0d7+WV/Fy9mT6ehaC77BtqpDgZI3vkM/VJyYelS7Mk5E++LlOwa6GBt7kzaQh50I/0YTTb+0dfJVdYkih3J42k1nY6fLVgLwF0HN+EKBZgen4Y7HKTVPczd9dtZk1POLxZeijsUOKV79l6oAK4oivIhEw36iHiGY1ObLPEI/eSBLNBdx8hbfyfqd6Ez2xFGC9LvxnPwZaxlS09oYQIEepsIDbQBYC09C8u05XhrNyKlRAhBxDOE5/AGEs6+DV/9FoZf+x2OBVefdM/t4+turF9H3aw1FMko8cn5hPxOIo1vEjqwHuFIpVRvYHHBHLZ2HmJ67WuUzTwfrWDehHLspzBq+9GuOkZCAT5TMHF0+Leln7UIfqlZ+GV2GWdnFLM/o5gde56nQAjmVa0+oay60T6coQALUnJoHu0lWafx6fwZPGFP4tHWahanxLrsR4M+XOEgc5KyxoP4n+q2TSjrouxp/G7xlQz6PTzSso/PTj+LvLj3f09wFcAVRVE+YDIcxNuwFX/nfiKufiI+F0QjIAToNDSzHc2RjjlzGsbsGegd6XhqXsdd/QI6WwKJ53wGzfr2SHHXrscIHGnAnFV+wrWCR2oJj/ZgzJ6JZk3AVr4Sz/4XCY2tGOauXgfRMPZ5V2LOq+LIfXfg3v8i9qpLTumzuHY9QZPBiCe9lDmeXvTlK4iM9hDNmkm0p5ZwTz1a+QqWW+2k7nqELP8Qhgu/zEHnAK3eURIMJhINZhINZkyahl7o0ITArNOjP2Zedzga5e62A8xNSGd+Ysb48Wj3IQpHu7k9s4LfdRzm+v52FqXmMT+9kPkX/TuDfg+v9TaxZ7CLQ8O9OAwmsm0Our0uAOYm5/CEc5AFKTmkFS/hs+nF/KNpD7sGOtF0ggSjBR2CZztqMGsGfrHwUu6cfhZ6nY44vQmb3ohFb0BKyYtdtZg0PanmuH/mf48pqQCuKIryATkauD2HXyPiHo6tHJaYgyk3Bc2WhAwHiHpHiHhHCLv6cO5+CvY+i2a2E/GNYsyagb1yzYRBZca0QoTBjL91zwkBXEpJoKuG8EgPCbNjrVBr+UoAPLUbMGaU4tr9NMb0UkzZMzFlTceQWsjwq78lrvLid3yWGw36GH7t91RnVJBotpHrN6Cl5KPPKCPa30Kkcz/RroNIVz/h/S/QZ8/g4eWf49Vd6+j2n3zTjxSjhXvmrqYiPrYhyYu9LXT6XHxr2tvd+pGeWjoPvooltZgvLrmGZ9b/kf/Y+TxnZxTT6IoNQOv2OgHQhKDUkUJ9OMTznU5C0Sh5tgQEEAEWrPgkpvg0AG4pXYAnHMSqN6ATOgKRML8//CZPth3g5uJ5lDhO3ESleqibIz4XnyxZgGWKHpR/lgrgiqIoH4BowMvg+p8RHu3FkJJPXNVaDMdMaYp4htCZHRPWKY8GPAT7mgj2t2AuWYI5r+qEoCp0Gsb0MgKd+5HhqydM7Yp4hgh0HoRoGEvpUgAMSTkY00vxHt5IXOXF+Jq3kXzRV2PlCo3E8z5H38NfxVu3Gds77L89+MKP6HQN0l5xMxcHPWiJmQiDGS17JrTshrgUok1b2ZFcxK+X/ht7NSMmv4/lyTl8sWQ+sx0pOENBhoI+9o70kWA0YdLpCcsof++o4cZdz3Pv3IuYm5DOn1urKbTGsyotHyklkZbthOvf5Lm4DOIzZnCrwcgP5q3mk288QpfXSYkjmcWpeUyPT2ducjaVSVnjgTUqJQN+DyZNz/b+dqx6A4X2t595CyGIM5jG35s0PbeVLeJXhzbzSGs1t5QuwH7MeV84xOs9TUyPT2NO8tvf6ftNBXBFUZQPgL/zAOHRXuwLr8WUXgzEdt/y1m1iZNNf8dZuwJheSspl38Y6/VyEEOhMNsy5FZhzK05atim3gkDnfvxdNVjG5lBDbPpYaKgDAEvR29OzrOUrGX3zfpzbHwUpsVWtxVW9DmNaMY75VzH04l0MPPVfOLNnEh7pJuLsI67iIpIv+QYIHZ0+N93NO2iuXk/XwushLoPpnsPoshYBoKWXoDOYaVpyEz+M6HkzKskw2fhh8VwuzyzBelwLdWS4h30d+8hMyeHyqljLf21GMTfueoGP717HvxVWccA5wP/MWIYuEiZ8+HWi3TUM5M3BZ03BHwowGvSzKquMmiu+ht1gQneS3gOdEKRZYt3czc5BShwpGCYZP3Ash9HMp6ct5tc1W3iwaS8X55STbYtHCMFrPY1IJFfmV5zWHcnUdqKKoigfgEB7NTprAsaxDTF8LTtp+9FKuv90E4HuQySeewcyGqb7zzfT9YfrCXTXTFqODAcJdB2acMyQlIPOZMfftvftdJEw3uadREZ7x59/H2Wdfg4yHGD4td9izp9D1DtKoGM/rl1P4u88RNLqLxMa6sDftgchdBiS8xh+/Q90/+12nm4/xM9qt/Lgmw+yLWMmA6XLmSsMGCNBtJTY4inCaMGfUsCnwho1mpFvTVvMxuXXckPu9BOCd9Q9RM/uJ9GkpOlIM966zUgpybLE8cjCteRY7Py8cRdJeiOXDrUQ3PBHokdqMVVcRHPBfEx6I5rQcXikF4B4o/mkwftYo0E/Q0Ev0xPSTil9htXBrWULkEj+3rSbext2sa2vjf1D3ZybWUq61X5K5bxXqgWuKIryLxYNeAkcqceU/3YX+MAz3yca8JB+02+wV12C0BtJXvMfjL75dwZf+jntd11EytpvkLDyM+N5wiM99Nz7mdhqaqu/SvLqLwGxLl9jdjmB9v1EA16E0cLorqd4qrueYUMc15csmVAfS/EihMGMDPmJq7oUX/0bGDPK0JmseA6spyN/Pvs/9wxrMopINVsBGNn0V9Zt+As7XvsDVWYbMxo3kXLzHzCXLKJ504ugaeiOmar1Wwz0SHhs+hIWTLFxStQzRGjHIwzozRhyqogMtnO4dQ9VOh1a6TJSdRr/SM/jq95hVodGMQ0H0BctQF8wDy05l6a67ZTYkwlGI9SM9LI4Lf9dfS9NzgE0oaM8Pv2U85Q4Uvl21Sp29new6UgjG3qaSDfHsSrr9GxgciwVwBVFUf7F/F2HkCE/5qyZAAT7W/C37iZ57TdxzH97a0+hN5Jw9m3Y519J7yP/wcAz38fXuI30G35BoPswR+67g2DQx+jMVcj1dwGS5NVfBsCcU4G/aQf+jv0go2yqfYN9mgXMDrZnzuayY+qjM5ixFC/BW7cJfVI2gc6D2OesxZCUQ4dm5NH67bitydQOtnNl4RzmJ2bQMPtidoahfMdDLO09jGHxDZjGfhjoPX3oEpIQlthOZTUjvfy1r51rCDPHP3zC/ZBSIl19hHY/hc5gYqB4GekGM7qENOraDMxu2Ul0pBs5eoR4KbkntQhD0UK07Onjz/hHg34G/W7OyywhiuTxlmpGg37ijeYTrjeVBtcA6eY4kk3Wd/N1YtBpLE0vYHFaHrUjfSSZrBjfxz3Wp6ICuKIoyr9YoH0fOks8miPWVeva9QQIgX3e5ZOm12yJZN7yZ0bf+Bv9z3yPth+fS8Q9iCGlgOFb/sITbjcXxKUh1v8MZJSk1V9Bs6eixSXjqd1I62g/r5niKfKPYnb3sclgY4ZrmFL723OTky/+D+IqVhPsqcWcV4UxJTY4bF1SEeGUXq4dbGFrWz8PdB5kpyONZnsa+XlVrM4pR+57FuOFX367vp4+tOJFCCGISsnXd60j3mjh/9kSiTRsJTrQji4pB+HIIDrcgTzSgPSOoLMmYFpxKwPdTZRa48mLS+QJzyje+GTiOvejL1qEvngh2iSrwdWP9qMJHTMS0tEJwVOtB6gZOcKStIJT+k7C0Sjt7hGWpxceHHGlAAAgAElEQVS95+fWOqFjxjFT2k43FcAVRVH+haIBL4GeOkx5lQghkFLi3PUEltJlGBKmXixFCEHCilsxF8zlyD8+j6V4MWnX/oSdA134XU42zLqYbKIMvfQL0GkkX/glTFkz6K/fwlM6C3GphZz/3HfRWRM4Yo3noc7DfKlkPnZDrAVrzJmN39kHIz3Eje3dvaW3hUMjvayuuogyWzyFfc1sa9nLG30tpHiHuWzaEox6A+S9vZCK9LvQhbzoUvIAeLB5L7sHO/nlwktJS84i3LSNaH8rw007eDgCa/Q6CjJK0c++EC17OiHNhDN0iCyrg3nJOTzXfoja7AqWz730pPt71432k2V1kGy2AVBoT+LwSN+EAC6lRMKkz8S7PCOEo1GmJ6Se8nf5QTutAVwIsRr4FaABd0spf3Tc+U8CPwW6xg79Vkp599i5nwAXExto9wrwBSmlPJ31VRRFOd0C3TXIUABzdqz73N+yk/BgO/FLP463fgvWsmUnzW/Oq6LgPzePv2/3usgymBmVEV6Zdy1XR0MMvXgXhuR8zDNX8XxPM664VK7b8ziG0R7M1/2My9NLuLf9EA901DDLkUKTZ5SmkV4CLbuZnVnCCp0eq2eUZ9sPUeJIpiopK/ZcPa+CFXkVzGzdg277I+j7GiBrxoT6RYdj/5xrKfkcHunlh9WvsSQ1n6sLYiOytTmx1cveaDuAv7uelswSyo9ZSW3A60TK2NrjDqOZ8oR0Dgz3sDyjkKn4I2G6vKNckD1t/FhVcjaPt1QzEvSRYLTgDQd5qHkfOgQ3l8xDO+7HwMGRXsyanmL7iXO6P6xO2yh0IYQG/A64CJgBXC+EmDFJ0keklFVjf0eD91LgLKACmAUsAE4+AVFRFOUM4G/bh87iQHPEBko5dz6OMFoQeiOeus1E/K5TLysSpqe/hcK6Daw2GGjxOdl79h2Yipew8dn/5Sd711OfmMP5Qy0kHnwR4+qvoOVVkmKycmFaATWuQR7rqqPJM0zOcDulQnIwLp1f1bzBzw5tQgjBJbkz2Nbfzn2NuzjahkrKn4M5pYBI/RZkNDKhTtGhDvyakZ+0HmL1y3ej1+n40fw1E7qlg5EIB5xDWByptPu9E/If8TrRhCDLGnt+viA5F2coQKdnZMr70OIaQiKZlfD24LOKxMzx0eijQT/3NezCGfTT53ezpbdlQv429zD7h3o4K73wX/Ls+v1yOmu6EGiUUjYDCCEeBi4DJp8LMZEEzIAREIAB6D1N9VQURfmXiAZ9BHpqMeXFWqPRkB/33uewzbqAiHsQodMR7KnDUjj/lMrr9LoIDnWSoNNTUL+JqmnnsnGoh5qVd9Lz1t+J2/U47WUrcW5/AK38HPTLbhnPO8uRQrLRQpzeiM3VS7juNYyzVqGbcR71zn4Oj/SSYrJxx9YneKW7AYDy+DQWpcbWBDfMXIV/891EOqrR58+NfT5nPzt76vl/8cl01L7F1QUV/FfVKpKOGxR2YLiHsIywMDGPrf1tuEOB8YVSenwu4gym8cFnMxPTiTMY2TvUTe4U64nXjfaRYLSQG/f21LhEk5VCexLVQz3sHOhEE4I7p5/Ftv52tvS1UBafSqbVgT8S5tn2Q+TYHKzJOXHp2Q8zcbp6pYUQVwOrpZSfGnv/cWCRlPLOY9J8EvhfoB+oB74kpewYO3cX8CliAfy3UspvTnKN24HbAVJTU+c9+uijp+Wz/F/gdruJizs96/Ge6dS9mdpH6d7oXEfQPL2EMirfOTHv7d7oBxsxtmzEN+1iotYUTG2bSdj0A0aXfBHN0w+akYglCX/Jhe9cGHBgqIHnBw6xwWzhftcIWZqVJzJnE0KyIBRiV+1j/CEv9mPgfAx8zuclWTPhy18BR5dfjUaw1T+PIIpn+hX4gP3+IbZ4e3nF04VZaFwbX8QTzhZmmhL577SxTUekxNr4EsI3hLv8CjT/CL1tG/mUPR6HzsQXUyuZZzmxO1pKyTOuNow6PWdZ03nG2cJyayaFxtic6XWuDgxCxxpH3nieLZ4eGgNO1trzcGgTNzmJSsmjzmbyjXZW2CYObjvgH2K7pxebZuDCuByS9GaC0QhPOFswoOMSey7bfP00B52sdeSTqrec0n3/Z5xzzjm7pZSn9gvtHZzOFvhkw/iO/7XwHPCQlDIghPg34D7gXCFECTAdODqJ8BUhxAop5eZjM0sp/wz8GWDatGly5cqV72f9/0/ZuHEj6v5MTt2bqX1U7o0Mhxh4/keEwwMkla/BlPHOc3iP3pvgUBeBzgPo4zMxpuShsyZMOopZSsngK/uJ5OWReNYKhBB077kLvyOdsrJyIoNWLKVLcR94iaRZReNbe57Mode347NYCEt4oWwu3+k9yJ1xAYwzVxH2jfCfvhYWjvYw12jkHnMSWy0Gvi38XOnbjWHeVQi9kXDTW0TsksNVV/Djrnp2DXQQikYxa3puLpnHl2euINlsw3FgA7+u2YJlZiFFY8uMRqZl4nv1d6RGa3GNtHB7QiI2vZlfpC5mzsL5tLiGGAx4mZ+Sg2OsRd3kHETX4uLm0oVUJmeyb89LSFsCM/NmxDYAOehmaUYRK/Nnj3/OyoCXnx3cxH4h+ERpFeZjurnrR/uxtrq4snwxsxInBvCF4SBZnbWszCgeH9wGkD/ay+8Pb+WATc+A28yNuXO58AxrfcPpXYmtE8g95n0O0H1sAinloJTy6GapfwGO7id3BbBNSumWUrqBF4FT24hWURTlXfLWbyHs7EVoBlz7nudUeiaFf5SRN//B4Is/xbXveUY23U3f0/9N/9P/jadmwwnpfY1bCfU2YJ2+EiEE4dFePDWvEzf3csIDbRgzSjHnzwEhCB6pf8frhz0jdDr78JpigemJvna8pSug8wDBN+/l5c330yvhlpxyvr7kY7x2zieYnpzD94QF92gfod1PEHX2EWnajqFgHv/VepCG0QE+XbaIh86+kUNXfI3/mXfReOD7ZMkCDDqNu+t3jNdBS8rBkDub6JF6/tPgoDUquaP8LDZ7e/jd4TdZ31XH7sFO/lq/gzZ3bP73joF2UsxWKpIy0QkdpY4U2sfOjQT9hKLR8effRyWarNxaugB3OMhTbQeIjn0/1YPdPNl2gFxbPGWOE0ePW/VGriqomBC8AabFp7M8vZA29zAljhTOz5p8YZkPu9MZwHcCpUKIQiGEEbgOePbYBEKIY38uXQocHnvdDpwthNALIQzEBrAdRlEU5X0WDXhwH3oFQ1oJtlkXEOpvwd9ePXX6oB/n7mew1DyJv3U3xoxydAYTcfMuwzb9PHQmO849T+NtfjvQRXxOXHufx5Ccj2ls9PngSz8HwD5nLVG/C2PWdPQJmegdqQR6at+x3keat+FGMKzpybcl4o+EeczoQJ9XhWZN4FFHNhlmG2suvBND2VKK0vL5TtUqvNEILxUtAVc/obf+jjDFUZ83l+rhHr4wYxnfrDyf5emFvHGkhS7P6Pj10ixxXJk/m0da9jEUeHvgmaFyDfelz+KlYIjVOeWMhnwkaCauLariPyvP4xsV55JqtvFg815e72mkxTXE0rSC8VHgpY5URkN+RoI+en0uBLER6McrcqTwscJK2t3DvNJVzytd9azrPMzshEz+fcbydz34bG3eTC7IKuOmorknjEg/U5y2Wkspw8CdwEvEgu+jUspDQojvCSEuHUv2eSHEISFENfB54JNjxx8HmoADQDVQLaV87nTVVVGUjy73odeI+l3YZpyHKXsmmj0Zd/U6ZCR8Qlp/dy2D636K59CrhJJKSDzvs/hqNzK0/ud0/eZqgt012BdcgyEpF+e2Rwj0NiGlxLXrSWTIR1xlbDR28EgDzm0PEX/WzchwADQNU2Y5QghMORWEB9qQ4eCUdZaREK0dh4jaU+nxeTg/q5SlaQXc27QL/eLr6Ft4LZtdQ9xYPG/CHtpzk7OZHp/GQ0O9mBZfjzDbMFWu4cHOOsyanisLYt3WXZ5Rdg6081jrfrzH1OPTZYvwR8L8o2kPAJFolPu7GvlRXzvTHKnMiE/nEyULuNCey7L0ItItdtIsdr4wcwXzk3PY3teORTNMmJtdGp+CJnS0uoY44nNh0LTxjUWOtyg1n/Ozytgz2MmewU5WZU/jtmkL39N2nSZNz6X5s0iZ4lpngtM6Xl5KuQ5Yd9yx7xzz+hvANybJFwE+czrrpiiKEnEP4a3bjCm3Av3Yc13b9PNwbn8EX9O28TnZEc8w7gPr8TZuQ7MlEr/sEwRbhgk7e3Huepy4uZcTcfbS99jXce58jLRr/hev38XIG38jbtYqfG17sU5bMb6ByMDz/0vYaOP1yksp6TxEaWIO2lir05xbgefQqwT6mjBnTQdiu5RFfaMIgzm213fXITrDIfxp6fhdIxQ7klmWXsAtWx7lxa5aDg73oBOC64vmTPi8QghuLJ7Lt/asp8aazOy138Qnozy5+xUuzplOwthz9wMjR7DqjYDk6baDXF80ByEE5QlprMwo5m8NO1mWXsh39qxn71A3OdZ4ri2s5N+mLSHTFs9GGidc16TpublkPsWOZPRCm7A1Z4rJRpLJQot7iGA0Qro57qQ7gV2cOx2BIMNqZ35K7pTpPgrOnAlviqIo7zNX9TqIhrGWLqfviW8T6DxA9ucexZCSh/vAy0gp8bftI9jfBNEolpIlWEvPiu3R3TLM0PpfIPRGUq/4LlpcCq6dj9P/9Hc58o/Pk33n44xuuRfX7qfR4pKwFMW21vQ178Bz8CX6V3+Nba4Rtg8f4bzCuVwmo+jGdvrSbImxJU2zpse633c/RXh4bAjR2AC5HmsSwuwAoNiezJLUfPJtifypdhvtnmFWZZWRaXWc8JmvzJ/ND6pf5YHmPfx4/sU817wPdzjITcVjU8GkpG60j9mJmZTFp/JA0x7e6mvlrPTYQiq3T1vEDZseZO2r95BstHJZ7kwWp+XxhRkrTtoSFkKwLL1o0uOljlT2DXWj1+mY8Q4bieiEjkvyJltS5KNHBXBFUT5SZDSCv/MQ3tpNBHsbsBQvZnDdjxjdch8A7j1PY51+LiNv3INz+yNojlSspcti3evHPJvVRtpw7XmKxHPuQG+PDaByLLwGgN4Hv0igbS/2BR/Dc3A9cRUXI3Q6pJQMPPN9tPgMGkuXYxvoIC8a5rVIlL667dxYPJc4gwlTzix8jdvwdx/Gs38dQjPiWHQNAh2RgIuQ302/L0BIxhZRKbYno+l03FK6gO/uexmAjxfPYzLxRjNrc2fwVNtBvlO5in8076HUkcKCsdZsm3sIXzjM3ORsZiRk0OQcZHNvC/m2RHLiEliRXsQVebNINFlYllbIvqFursqveE/d2EeVOlJ4q68VgSBrkh8dyuTOzCf3iqIo75KMRvE2vMXAcz9kZNNfCDt7sc44D8/hDYxuuY/4FbdhyChj6JVfo3ekEb/0ZhLO+QyJZ38aa+nSCcEbIK76fnRGG4nn3THhuH3uZejjMxje8AeMSdkkrrgNQ0Jsgwv3/nX42/ZgvvAr1HpdlAfdXGixcHHJQmpGevnloc2MBHyYcyuRkSDu3U+jT8rFteJTvG5JRhQvwl5xEb6ZFxAw2vCEglj1BjIssTnU1xZWYtMbybMlsCLjxNbuUTcWz8UTDvLjAxvYM9jFDWNd5AAHho/gMJqYFp+GEIKrCirIsjh4ou0AvV4XQgh+u+QKvjprJfuGulmQmktp/D+3fniJIwW90CGR5NhOHMCmTE61wBVFOaNIKRneeDfhgTbQNIROQ1gcJK38NDqTbdI8gb4WXLufJDTQij4xG/v8qzGmlzC47seMbr6b+OW3YsqtJDzag6d6Ha69z+KYd8WUdQh0HsTc9gYJF3wRzZY04VxsC9BPM/Ds9/F37MecWwFA2D1I/xPfxpg5nfayFQQ665jh6kOfXkxlai7ptngeaNrLH+ve4nNlizFmTMOQkk9P/nz+2rQLTzjEweEj3FK6gDb3MBJJv99NUVzyePB1GM38celV2A2mSTfsOGp+cg7THKn8tWEHRp3G1QVjdYxGaRgdYGFqHvqx59AmTc+tpQv5U91W7m/azdrcGZQnpPFyVx1WvYFLc2ee+pc3hUSTlVRzHP1+DxkW1QI/VaoFrijKGSXi6ifQdRBdXBL6pFw0RxqhviY8tZtOSCvDQUa2PsjQy78k4hrEPu8q4pd+HFNGKd66zQy/8hscS24k5dJvEe5vxpBSiD45n+GXf4WMRie9voxGGXj+R0SNcSSsvH3SNI6lN6Iz2xl+/Q/jeXr/8QWi3hEyPv5r9oz0Yxrp4vJglH93e6kf7SfD6uC6okp6fC7+0rgL6zmfoaNgAX9p3IVdb+a6wkpGgj5+UbOZbf1tWPRGWt3DFDuSJ1z73MyS8e7wqRwdzAawJqd8fKnTZtcgwWiEucnZE9KnW+18adYKCu1JPNl2gKfaDtLoHGB1Tvn4Ai3/rBkJ6aSabdiPGeCmnJwK4IqinFH87dUgo9jnrMVeuQb7nEsxZkzDW/cG0aBvQlpPzWv4Gt7CUryExHM/gymzLLaFZzTCwDPfx5CcT+pV3yfY14SMRtDMcVjLzybY24B7/wsnXFtGo/Q99nW8tRvwVNx0Qrf6UZrZTvzSm3Dve57QYDvDG/6It3YDKVd8F09iDg1N25DDXfSi4+WRPs5d/0fu3PoUwWiUawoqaHUP8bvDb3JPww4SjRauLarCYTRzW9lCEgwWWlxDpJptdHpHKbInTVqHd3J1QQUrM4q5o3zp+LGDw0dINFnGV1o7lt1g5rPlS1mWXkjdSB+5tgSWpU29Q9i7dUneDL4wc/l73ov7o0gFcEVRzij+9mr0iTkTusstZcuI+l1467eMHwu7B3HXvIYpexa28hUI7e1BVs7tjxDsOUzy2v9EpzcR6DyIZk/BUrgQzZqAPrWIoeNa4VJK+p/8Fs6tD5B4/p14p0/dxQ6QsOI20Gn0PvI1Bl/4MXGVFxNXtZatb/ydqM+JO70UvdDx1iX/zh3lS1jfVcc1G/5OYVwSl+fNotU9RKopjhuK5/BQ816WvvBbQtEoNxXP5ZzMYvJtiUhiA9imMuj38GDTXg4O9Zywuly80cwDZ9/ArMTY8/lgJEKTa5A5SdlTLmyi12lcW1jFbWWLuKV04fu6AIpBN3F6mfLOVABXFOUDFfW78bXtZXTbI4y8eT+B3sYplzINO/sJDXVgGpsffZTBkYYhrRhv7WaiodjqzK49z0A0im3meROvF/Aw+OJPMRcuIK7yYqIhP6GBFsx5VZjy54CUOBZcQ7D7ML0PfIHRrQ/g79jPwFP/xeiW+0g89w6SL/76+HSusHsI1/4XiYb8E66jT8jEPvdyfPVb0CdmkXrNj3DtfIyDoRDZJUtoEHqmxaeSZXXwzcrz+a+qVfT4nLR5hilPSOOz05dyY/EcTJqerf2teMMhXu9pQNPpWJyWjzMcu95U+1eHo1GeaDtIt9fJ852H+VvDTrpOsiVng7OfiIwy57ju8+MJIahMziL1DF4A5f8KNYhNUZQPRDToZ2TzPQR6GyASRhjNgMDXvAtDSgG28hWY8+cgjlnUw98R6z43ZZ648YS1bDmjW+7D17gNLT4df3s11vKzTxjYNvza74k4+8i69a8IIQgciW2Vac6fgyExGy0uGZ3ZgX3eFXgOvYpr95PjeRPO/jTJa7853s0ro1Hce54m7OxD+j3YF1w1oQu4Y8nHeTIQoqzqYua2VWNwD9OXOZvz8mbx0zce5pLct3+IVCVlAVA91E1BXNL4oiqxYz0AvNxdz1VjA86anIMA5NriOTTcy7T41Amrrr3aXc9IwMtnyhfjDYd5oaOG+xp3MysxkzU55RPSDvo9vNxVT5bVQf4UW3YqHz4qgCuK8oHwNbxFoLsGa9kyjBllaI50iEYIdNfga9rGyJb7sPa3ED82txrA374ffUIWOvOJrT9DYhaGlDw8tRsQejOaJR5zwXwC3TUgNDR7CjLkZ3jDH4mbcxnmgtggrkDXQfSOdAxJOQghMOdW4K3bTPr1PwedntBQO4HOAyAlcVVrJwRof+suws4+zPlz8Lftxd+6G0vhfKSUvDHQyTNuJwlLb6TO72Vfww4sjjSkLRmr3sBoyE/lWNAGKE9Iw6TTqB7q4bK8WePHj/hcHPG5sOoNbOhpIhAJY9L0NLkGSTfHcXi0jw3dTaRa4rg8bwZpFju1I33sGexidXY508emsM1OzOD1nkbWd9YyHPDyscJKLHoDI0EfDzTvxao3clvpIvUM+gyiAriiKP9yMhzEU7sRQ2oh1mkr3j6h6THnVmDKmY338Aa89VswZU3HnDOLsHuQ0FA7tvKVU5ZrKVuO860HAIl9wbVj3d73TkgjNCMpl8RWcI4GfYQG2oibfeF44DLlVeE5vGF8KVNjSgHGlIITriWCbry1mzDnV5Gw/JOMIPEcehUtIZuXfB5e72+nNC6RSzKKCe9/gWZNUls4h2R7KvXOfoAJAdyg05iRkEH10IRNG9k/9v5TZYv4dc0W3upr5ZzMEppdQxQ5kjk80keuLR5/NMw9DbtYkpbHrv5OSh0pXHTMFplGTc/qnHIyLQ4eaN7NvQ07uSR3Os+216AXOu4oX6K6xc8wKoArivIv523cTsQzhDG3AhkOIvTGCeeFEFjLzyY00MLo9kcwJOcTaK+GSBjjJN3nRxmScjFmlIJOz9DLP8e952kSVtyGuWAeEfcgEfcgpuyZGJJj06wC3bUgxHhrHMCYko8Wl0xjazUh69sjvOP1JoriYmuZSykxdWxFJFhwzI91m8cvuo7egQ7+P3vnHV7HVef9z8zt/UpXV71aXbblIveeOE5PCIlDEiAhZYFQF95d9qW+sOyysEuAhVCWQJb0kAppjuMS9ypLlmyrWL1LV/Xq9jrz/iFZliw5ySaExcl8nsdPopkzZ87M9fVX51efO/IsZ9NKWZqYwWZnNtJYL8JAE2ULr2ZRyVoAvntyB3rVhA98OosT03mmo4a4JE0FiNWM9qESBD5bvIrfnT3Gjt4mLksroNU7wpb0QlxBLx/LW0SFI5PnO05xZLATs1rHnQXL5gwyW+RIx67V8/umYzzWUo1Nq+dzxatJVSqgXXIoAq6goPBXRY7H8DfuAUGk79cfQ5tSSPJtP8aQt2zivCwTOLsf977fY1l2MzFXM57jzxIPeVHb01AZrETdfYha06w0LkEQMJVfy8AjnyXQ8CaO679J4hVfuOhawn11qO3pqG2p5+cQRULpZfyh/gAxwymYFEGtqOLbJauxaXSEe+tQefuwbPocKqMNTyTEjr5mDiXOQ2qvZENPLUv9/cTG2pEG2xBtKahL1k/do3a0j/n2VELxGHFZRj/ZCnNRYhp/aKmkxTtMsS15cmw/RVYndq2BTanz2NHXxD+GNuKOBDGqNagFkYUJaZg0Ou4qWMZSRyYWjXYqt3suciyJfGX+Bl7trmdj6jwyJ38xUbi0UARcQUHhr0qw/QRxzxCRwWYErREp5KPnFzdhW3cPpvlXMLrj54TajgEQ7q0j5RP/SbD5MAgCxuKNRN19dP1oM7Icx77ubuybPoPakkQ84MZb+QLuQ48SHWoj+bb/wLb6ExddhxTyERvtxrLkhll+39OmJOKSxCcMBsxpJfjiUZ7srqdufJgVegP+M28gmVIwFKym2zfGLxsOEYzHWJpRzKq0PHR99Ujjg8R76qmW4qxec9NUGltMkjg91s/WnHJ+2XAIZNCr1dg1BgzqiX+Sa0b7KbYlI8syNaN9XJ6aT+VQN1vSi3i99yx/7qqbeAYJ8iyJJEyKtSAILExMe0efg0Nv4lOFy/9nH57C3xSKgCsoKPzVkCUJf8MeBJ0Jf91ubGvvIum6/8vwqz9k/OAfGD/w36isKTi3/gBdWik9D96Mv2EvutQiokNtaNNLcD3+JWQpiqn0csbe/DXu/Q9jKFxDsPkwcjSELnsx6X/3CKb5VyBLcaIjXWiScmeJdKj7FIgq9Lkzm35IssSJSIQccwLOpn2obSnY7Wk4tAZqR/so7T6BoDUSzluBIIrsd7URl2XuL1l1PnI8a6Kv9itdddx/5EV+Golw2+T8TZ4hQvEYTr0ZSZa4LruM8UiITv8Ybd4RjCoNtaN93Ja3iC6/G3ckiF6tYUdfE5el5iMKAr9rmvgFR0ae4UdX+HChCLiCgsL7ihQNIwXHiQc9RF0txNx9SCE/SDHs6+9G1JtJ3voDrMtuIeJqxrzkRsRJIbSuuoPx/Q+T9X9ew1C4lsCZHQQa3sT50e9j33gfEVcLo7t/RaBxH5blW7GtuRN95kQEtxQL4618gehQO6byazDkLqXV52YwHKDCnkyoqwZtajFq88xCKGfHBxmJBPjoyq2I1X8iVvU8wvLbKTLZqKzfi1cOk3XFF5DrOwnFotSO9lNqT56R9nWOp9trAPhVwyG25ixEJYpTQWp6lQqLxsiWjCIABgIefnhqN/kWx9SYmsn/qgURg0pD5Ug3FY5MKoe7USGQoDVQrgj4hxZFwBUUFGYhRUKE+88iRINvP5gJv/Vc6Ueh7lO4Dz2BHAtP2HsBlS2Z8WMPYiy7HG1y/tRYfe7SGcFkAI7rv4GvdhvDL32f1E/+gqE/fQ/9vBXY1t8DgDalgJTbHyAy3IkmIR1RM1GXWwr78Rz7I3G/G7U9jWDLEXRZi3iut5H+oJ9dHbWs9Y+zbtnNs9Z8dLALvUpDQVI2woZ7CO15iGjlsxQlZHAk6KFn6XXkJ+dBfSe1Y30EYxEWJcw2W3f73ewfaGNRQhq1Y/283tvI9VllnBzpw6LWEozHqJhWszzFYMGpN+PQmzg82EEkHufUaD9aQcSk1rIpdR67+1vItyRSOdxNgs5IniXxLX3dCh9slEpsCgoKwIQIhweacR9+kqE//T/c+36HrnUXshR/y+vCA80MPv9tAm3HZxyPDLbjPvQ4KqMd8+IbsK29k4TLP4uot6D6PhMAACAASURBVCF5h7Cvv+9t16Q2O3Bc+08Emw/R8+AtyLEwKXf8BGFadHWw+RDeo08zuuPnjB95imD7CdwHHkEK+bBv+jSWZTcjhbw0tFfhCgVYmZiGaqyHl9HyK/coff7xqbk8kRCnxwYoT0hDJYqIRju6Dfciag3Yh9pITMmnyXA+4KtyqJtEnZG0OSK4n2mvQQa+u3gLeeYEHqw/hCzL1I72UWxLJiZJlEyLQhcEgfn2VIxqDREpTuP4ILWjfWSZEzCqNWxOL2Jtci5qYaKwjUWjpfwd+rsVPpgoAq6goABAoP5NRnf9klDnSTSpxRjnX4Hoc+Gt3faW1wXbjhMPjDF++Em8tduQZZnouIux/Q8jagxYV96GPqMMTWImKlMi4wcfQZOcjyF/JWP7Hmb82DOEumqRwoGpOWVZRo5FALCtvRNdxnyiwx04rv0aWuf5PtdSJEiwvRJ9zmLMC7Ygx6P463cjiCoSrvgi+vQSdOmlaBKzONI60TpzrTWJ230uPpJVxmgszC8bDtHpHQXg+FAXESk2o5yoypKEbuPfoV96E8VFazk7PkQwFmU8HqHFM0x5Ytos60NcknimvZaKxEx29jXz0eyFnHEP8EbvWRrHB0kzWNGp1LPKoM5PSCFpsnJc9Ugvp8b6SdQZyLc4MKg1XJtVSo45gVJbMkXWZBYlvnXZU4UPNooJXUFBAYBgz2nU1mRsa+5EmExrip6pw1+3C21yPvqMslnXyLHoRG/srHJQafCdep24Z5DoaA/EIljX3zPlzwYIdVQT7jqJ85YfEHX3EfcOghzDd3o7nNqGqLcixyPI0TDIEsbSyzAWrCb1zl/hO/Ua9o2fnrnm1mPIUhzz4hvQ2FMxL7qOuHcIUWdGnBaZTclGGvc9zkI5ijDQiCDIlJasI9to56nWk/y68TD3Fi3n+FAX2aYEEnQz/dkqqxOV1UlZwMPx4W7q3S5aIh7iRhUL5zCf73e10RfwsCElD71KjUmjI81g4RtVrxOTJUwaLbnmBPRqzYzr5lkcpBusmNRaXuw8jT8WwarWM3+y4YhRreW6rFLGIyFyzHaS9HP3P1f4cKAIuIKCAlIkRGykG33e8inxBoikV6AOVDJ+5Ck01/wjKtPMfOGwqxkp7EOXuRBNUi5qswN/3W4ElRrr2rtm5GnLksTI6w8g6i1Yl28l2FE1WRXtm8T9o4R7zhDzDCFq9QhaIxFXC8HWoxhyK9CmFpKY+pUL1hwk1FGJIXcZmslyoYIgoLYmz3q+M4YEoloDCwdbiBNHlZSLyp6KFbirsIInW0/y28ZjxOQ4N2bPv+h7SjVYsGj0nBrtoyU8Tr45f84OWk+1ncSu1WPXGimwJtHgdnHHvCX8tG4/AHpxdhEXmOj2VWxPJllvpmqkB4A0o5X59vN56iud2bR5R+e8XuHDhWJCV1BQIDLSiRyPoHHmzjwhqrAsuwU54sd96PEZ7TUBwl21CGodGkc2giBgmLcC69q7sK69C40tZcbYkdd/TODsPhzXfx1RbyY21oM6IQNRo0NjT8O8YAv2NR/HuuxmLOVXY1lyI3IsQrCjes41B1oOgyxgWnDljOOugJehoG/qZ1mWOTbcRWZqEQleF3iHUecumzpvVGu5s6ACp96EWa2lyHpxYRQEgUJbEqfH+vFKERY7ZkeAD082BlmamIlDZ+BTBcuw6wykGaw4dEYStAbMGu1UoZYLWWBPxTFpPdCKKubbU3BM22mLgsgn8peybFoAnMKHE0XAFRQUiA40gyCisc82B6uMdkwLryXiaiLYUTV1XI7HCPecQZtSMKNjmDYxY5Z4e2teZWznL7CuugPb2k8hyzKx8QE0SdlzricuSTw22k9nUj6htqPI8diM81LYT7ijCv285WimCaEvGubBhgP8+MweTgx1A9DhHaUv4GFp4SpEcyKCzoQ6a8GM+fQqNXcVVPCZ4tUzunTNRZktmagkoUVFoXV2K8/nO04RkyXSDFZWOXMxaXSscubS6RvlhxXXckvuQmxaA5km2xyzTzQ1SZ8MinPqTSxU0sQULoIi4AoKCoQHW1Hb0qaqhV2ILqMMtS0N36nXp4LLwq4W4iEP2oyZJmcp7CfmHZrq6R3uq8f11FfQ51bg3PoDBEFA8o8hR8NzNgkBaBx3cWqkjxpHHlI0TKirdsb5QPMhQMS8cObu+89dZ/BEIzh0Jh5vPcGfOk9zcLAdtaCi1JGOdtkt6JbdjDCZbjYdQRDQqlSzjl9IhsmOVasnX2dFI84c3+od4TeNRyi0JpFsMLE2ZeL51qXkolWp0anUOHVmCq1JiMLc//yaNTpWTO6uk/VmFthT5hynoKD4wBUUPuRM+L+70OdVXHSMIAgY52/Bc+hR/GcPYJ6/mXB3LYJaO0OEZVmm58GbCfecQdRb0STnExvvRzTYSLvnd4jqCX9xdKwHRBUaR86c9zs+1E1MlmiRZeLJ+QRbDqPPWQyAv+FNQu1VGEs2zCjC0uAeoHKoixxTAquSc2j3jrK7rxm1qGJhQuqE2KYWvuP30uIZRpJl0o3WGX5uURD4TPFKGt0zTfvdfje37XkCSZZYl5zHosT0qRKnNq2BJY4MTgx1E5fli5rPz7E6OZcr04uYb08hS6lTrnARFAFXUPgQEWg7jhwOYCrdNHUsOtI14f9OynvLa7WJGWhTi/DX7cKQt4xw92k0yTPN54GGPYR7zmBd/QkElZrIYCtqOYXkrT9AZU3GG42gEUWiI92odGZUltkmaE8kxBn3APlWBx3eUXqzK8h2PUOw5SgRVxMxzyCmBVuwLLp26ppwPMbz7aeQEfhezQ4KrEm8tuU+0o1WDg12sNI5Yarf3tNIl9/Np4su3ve60T3Iv5/ew46+pqljNo2efKuDj+Uu4uachZg0WsRp1w8Evdy25wmC8Qj/suRqmj1DbEiZN2PeDSnzqBzqQiUIbxuANj8hlTJ7CiucORfdqSsoKAKuoPAhIdhVy/iRpwDQphajmUx/irgm/d8J532tsXEX7oOPYG05Te8JASnoQTTYSL71R4wfehT3wUeIB8cxzt8y4x5ju3+Nyuok4fLPo3XmEo7H2O7qoDvoYbDhEP5YFJ2oomKokw2OrDlFtGqkh3A8xlUZxTzeWkWjqCU/eR7+xj2ojHYSNv4d+sla4+fY3tNIf9DLPlcbvliEmtE+On1jlNiTKbFP7HYlWeZb1dsZCHoJx2N8qWzdjDn6Ax5+dHoPL3ScwqjWstqZQ4UjE6tWR99k+tjXq7bxr7W7uCW3HJs3yNEmiXA8xrMdtQyH/Ty2/nYqh7vJNSeSa0mcMX+WyU6+NYlgLDK1M78YyXozN2bPZ75iPld4CxQBV1D4EBAZ7sJz5EnU1hTivmH8Z97Avv5u4Jz/OxVBpSHmHWZs968YP/QYcjyKxpRC3J6EqDUSqN+N98Tz6HKWEu6qQVBp0DrP79pDHdUEW49gLN2Mt/pPJGz+PDsGu9kz1EWGwUyu0UaS1kCv381+r5sacwpX9jWzIXUe6sldvCzLHBvqJMtkJ1FnpMjqpMEzyNbFN6BuOYRl0fWozDOFscUzxN6BVjp8Y9S7XXxn0RX8S+0uXu2u5wula6fGHRnsZCDopcDi4Een95BisPCxvEUA7Oxr4ivHXiYUj3JX/jJMai0GtZqIJPHxeUtJ0BmQZZnqkV4ea63ij20nCUtxGJ3oCmbT6PnDutto940SlSS25pbP+uVEEATuKVxO7IJI/rkQBIHL0gr+5x+0wocKRcAVFC5xZFlGCnpm9cY+R9w/hnvf70HUYF3xMULtlQRaj2Ic7UVtSSI20oUuZwm+U9sZeOKLyNEwlmW3kLDlS1R2BymtmIjY7nv4PkZ3/ZLsr+0k0nMGTUrBjJzxsT2/QdBb0CbPQ45F6GjYxwG0lNucXJ1yXuiXyDEqhBjH7Cm82Hmadt8onypYhlpU0eEdpT/g4drMUgBKbE5qRvro1JooW3vXrGcbCHj476ZKhkN+Xu9t5BPzlnJ/yWpe7a7nlQsE/MXO05jVWl654l4+ffh5vlb5Kgk6A4ddnTzUdJT59hR+uuIG9vS3ohXV3FO4nN+ePcIbvWe5fd5iBEGgIimTiqRMfrD0aqqPVbJweQX6yeC0ff2tdPnG+Hj+UrLMCXN+FpY5gucUFN4tinNFQeESJ9RVy9DLPyAy1DHrnBQNM7rnd0gRP9ZVdyBqDejzVyKIavxn3pjwf8fCaJPyGNn+E9T2DHK+sZeU2x/AX/MqhsaXCLZXIcciJN34LeRYhLFdD2Lf9GnM5VdP3Scy2Irv1OuYyjYj6EzoMhbwSttJiEXYeEG+sjzei1Ol4rbSdVyTWcLJkV4eb60iLkkcHe5EJYhTZu9sUwJaUcXpsYFZzzYeCfLQ2aP4omG29TRSbHXyz0smotKvzyrj9NgA7ZMlUoOxKK/1NHBdVilWrZ7fr72VIpuTuw88w0NNR7m7YBnPXXYXhwe7APhM8UpyLYlcnVlCu3eEVs/IjHubNTocaj0JOgMGtYaz44McGepkQ0o+K51zB+YpKPylUQRcQeESJ9h2DCnkw1vz6lTq1jn8dTuJjXVjWbYVtTkRT+XzjO3+NYb8VYS6agk07gNBRAq6ifTVY19/N9rkfEJjvbwSDNAoCvjqdjG68xdEhzuxrf0UnmN/JDbSNRVRDjC257cIKi269DI0Sbl0Fa6nHYG1XhcG1UxDnzTWj2hxIuhMLHFkcHVGMVXDPTzeWsXJkT7K7ClT6VkqUSTf6uCMux9JPm96DsaiPHT2CO5ICLUoMhoJ8OCqmzBMlia9IWui7Our3fXAhIncGw1zc86E79yi0fH4hju4JqOYh9Zs5WsLN/FMey2+aJj7ilaQMVlxbl1yHlkmOzt6z06ZvodDfl7rbmC3r5dn2mp4pq2GV7sbKLY5+UjOxau4KSj8pVEEXEHhEiYeGCfSdxa1LYXIQBPhvsapczHvMP76PegyF6JNykGWZUa2/4TR7T9BZUpAUGsJ9zeitqbgPfkyqDRYlnwEgCOdp6iWRXYll7B9wTVIWYsIth7DWLIJ0Whn6KV/njDdR0P4G/fhrXwec8VHkUIehNQiXhnqwunMY8FIG5JncMaa5fEBVImZUz8vTcpkS3oRVcM9BGIRFl9QuKTUnsJ4OESHdwyAwaCXh84eods/zq155dSO9TPPkjhVLxwgw2SjwpHJy5MC/mLnaVINFlZP2x2nGiz8ft3HKLOn8HDTcWJSnM8Wr6JgWiU2lShyU84CPNEQO3qbeLrtJA+dPUqLZ5gwEjIgI1NiS+ZTBcunfPkKCn8NFB+4gsIlTKjzJHI8gnX5LXiOPoOv9jV0acUgCHirXwIBTKWXAxDpbyA2MmEidu9/GMvyWwnU7UKdlcHozp9jKtuMypxIIBZl92An2eYE9KZUasJBuk2pXJVajKm9Et2m+xne/gA9D95K91gfA2od7ozFWDMr0I52IUbjuKJB7lx8NeoDvcTO7kWz7FYEQUAO+SDiR3TMrMC23JmFWhQZCHpntebMMyciiiLVIz00eYbYOZne9dHsBaQbrRwb6uKWnJlR6TCxC/9ezQ4qh7vZ09/Kp4tXoppWZU2WZY4Pd/Nmfws5pgTuLVoxZ2/tIlsySx2ZnBjuxqEzcVP2Atak5HLcf5hNCza+tw9QQeE9oAi4gsIliizLBNsqUSdkoDIlYijdiK/6ZUJdNQgaA6HuU5hKL5vqyuU7/QYIAubya/BUPk/ilr9Hl7sUKRwk7hnEunwrADv7W/CHPNyasxZ3OIENBUX8ubOOJ/VJSFIbRGKw4AaIhZHTy7E6snGkFhHuq8ej0hKRocKRSVZCKtGyzYSr/kT0xHOocisgHgMEVI7Zdbynt/CcjlalItds5/BgBxEpzgJ7KldkFGJUa6kc7sYfi0xVPJvO9VmlfK9mB1859jIxWZoyn8NEqdZtvY2cGR1ghTObj+UtQqe6+D+HH8tbTEVSJsW25FnV1xQU/rdQBFxB4RIlOtZHdKxnqpmHLq2UoOUwvlPbQRBQGe3oc89XV/Of2YE+ZylJN3wb36ntuA8+gvMj36H/sS8gGu0Yyy7HFfJzoLeRBbJEamYp7tYRUo1W7iteQZfPTdBkwV/7KvGFV2DPWUq63oxJrUGWJCJtB9BklaObv/78InOX8fpQDylDLZRWv4QBEDQGBOv/LL95pTOHSFxiQ2oe2dMivA+62hGANcm5s65JM1pZkZTF8eFuSm3JlE3mVAdjUZ5rP8VA0MMN2WVsSS+6aFGXcxjUGhbM0TZUQeF/E0XAFRQuUULtJxAQ0KVPpFwJgoCp9HI8x54BZKwrb5+qkhYd6yXcfQrH9d9Ek5SDecmNjB9+HPv6e/Cf3j4xVqXl1Z4mhMAYa7VaXvN7scaCAGhEFflWB7JlDZHxXqKd1WiyyhEng8YkjwsiIVRpJTPW+NvmY/ywqwEAFRaK1CrutqfwybfZxTa4Xfy0bj+heAyn3oRTb2JJYsYM8QY45Opgvj11TtM3TJjRjw93T+2+h4M+nmmvJSrHubtwOYsvsutXULgUUARcQeESRJbihDqr0KTkI07LLdY481BZkhB0ZrTJ50t5+s/sBMC88CoAEi//HL7qP9P3+7uRoyGsy7fS5BvjzPgQ6yI+hhIy+Nyxl7jenMUmzu+oBUFAu+R64kPtRCufRV1xCyp7KvJQK4Jai2pa+dBuv5uf1e3nyvQi7pi3mJOjfWzvaeR7o4PcEotORYxPxxMJ8ZO6/fyh+TgWjZ4sk416t4uRsJ+oJPHy5nuoSJoIgAvGolSN9HBP4fKLvqdbcsvp8I1xx7wl9PrH+WN7DWa1jvuLVl80V1tB4VJBEXAFhUuQcH8Tcf8YxtIrgAl/eKjtGGNv/hf+up2oHdlYllyPSm8BwH/mDTTJ+WiS85ElCV3mAowlmwg07kWTnI8uezG722owCwKLQm5+a5uIBD8RGkaW5RkmZkFrRLfxPsIHHyV2/I+w+AbiQ22oHDkI2vM74e9Ub0cUBP614moyjDauzChmtTOHO/Y9yX5XG1dlFM94pmNDXXz28PMMh/x8Mr+Cf1q4aWpn7Y9GWPHqz/l142EeXvcxACqHu4lIcdalXLyGu02r5/tLr6LNO8ILHadJ1pu5v2T125YyVVC4FFDSyBQULkFCHVUIGgPa5DwCzYfp/tkN9Dx4C6GOKmzr7yE22sPQC98GJlLNAs2HMS+4Cu/JlyfKpMoyCZs/D4B1+a30h3y0+N0sFWRE4M+eMdSCyEAsSIdvbNb9VZYkDJd9FlVSDrHqP4NnEHXaeUF+o/csO/ua+Yf5G8mYViFudXIOVo2O13saZ835g9rdaEU1r225jx8tu3aGWdyk0XJP4XK2956l2TMETPi/1YLIyov0FD9Hg9vFs+21ZJnsfKlsnSLeCh8Y3lcBFwThakEQzgqC0CIIwtfnOH+3IAhDgiDUTP75u2nnsgVB2CEIQoMgCPWCIOS+n2tVULhUiI4PEuo8iTohjYHHv0Tvr24l7h3CufXfyP3ucZJv+VcSr/wK3srn8Va/RKBhD0gxDEVrGO2tZ2Csj5i7D2PhWjK+8Cz2yz7DkdE+BKAsNMZpjYnOoJfPl64BYN9A66w1/EvNLl4Z7ES/4V402YsR1HpUk774QCzCd6rfoMTm5L6iFTOuUwsim9MK2NnXPKMmeLNniKqRHu4tXM6iC/LAz3Fv4Qr0KjW/bjgCwKHBDpY4MjBptBd9VyeGu/lzZx0ltmQ+V7JmRltQBYVLnfdNwAVBUAG/Aq4ByoA7BEEom2PoM7IsL5788/tpxx8DfizLcimwAhic41oFhQ8dvtrXCPecYvD5b+M/vZ3Eq75Kzjf2Yl/3KUStAYDEK/8efW4Fg89+nfHDT6CyOIlHwzyPikcELQOtlQAYC9cSFkROjLkotTjQjfbwitaKXqXmc8WrSVMb2HuBgDd7hvivs0f419rdSKIa7arbMV73j4iTBVD+s+4AvYFxflhx7ayUq5e66rBqDbgjQY4NdU0d/2NbLWpBZGtu+UWfO1Fn5BPzlvJi52ka3C5OjfWzbo70MZhwKezqbWZnbxMVSZl8unjVnD53BYVLmffTB74CaJFluQ1AEIQ/Ah8B6t/uwkmhV8uyvBNAlmXf+7hOBYX3RNQzhK/6JWyrPz6Vc30OOR4j0HQQtT0dbfK8Gc0/ZFlGCnmJDHUQHWwhMtiGFBhHtDrRWJyorcno5y1HZThf2CQ80Eyg6QD++jcx5K8k+fYH0CblzlqToFKT+skH6frxlQRbj2BZcRud3XX0mZzEBZEX+5r4YjSESqPnxNgAgXiUCqOZSMDNaxqRK9ILsWr1VOid7BnsJBKPo1VNiPHTbTUA9AbG2dXfPOHLNkyYyYdCPh5uPs4tOQtZ4Zxp2m7xDNPgdmFSa9GJKrb3NrI2JZeoFOf5jlNsSS8kSW96y3f9meKVPNpygs8feRFJllmXPNv/HZXivNRVR4tnmC0ZxVyfVar01Fb4QPJ+CngG0D3t5x5g5RzjbhEEYQPQBHxVluVuoAhwC4LwIpAH7AK+Lsty/H1cr4LCuyLcVUOo5xS6zlKMRWtnnAt11eA58SIgI+rM6DJKURkTiY33Ex3tIR4ch3gcQa1BbU9H7chG8o8R6q5FCvkINB/Gftln0NhSkCUJ78lXiPQ1gCyTcsfP0DiyaPGNUe8ZYXNyDqZpu0xNUg7OW/8N1xNfRpdeRpVnBLUjm8sdGeys/BMHmo6ysWwjh0f6yDCYSfINs1vQMBKLTlU2qzAk8aqvi6qRHlYn5xCJx3mu4xRXphdxaqyfR5pPzAhGe+jsUSJSnK9MzwVnQlTf6DmLSaPFF42w2pnD9t6zfH/JVezqa2Y47Of2eUve9l1nmuzclLOA5ztOoVepWZyYTqN7kKqRHoKxGCEpSjAWBeBjeYtYm5z3tjneCgqXKu+ngM/1rZEv+PkV4GlZlsOCINwPPApcPrmu9cASoAt4BrgbeHjGDQThM8BnAJxOJ3v37v0LLv+Dhc/nU97PRXiv70bfvBtxfITON18k1Bedea5pG4RjRDJXoBrvQn3iAEIsjGSwIxkSievnIVmTkQwJCBEfqsE+YklLwABicAx960545FuE869EDI2hazuOoesUkZx1bG8b4kRdI13RCQPVnqYmrjKnYxanmYrFEsSbn8DVdYjjOj05PjWmWJyksMBz9ZU0eTQ0BgbZoHPQ33mA57QqLDoNyV3j1HWfoDRuQIXA81UHsSYMccA/wGg4wIaYlVStzGOuZt44sI9MjQlPPMIfeo+z0ZBKsK6dOtqnllETGqErNMoqYwpHAy5yULE34OHP+9/k8fFmHCodyZ1j1HWdeNv3fWXEyvNAkdrKr/e8xkAsgF2twyZq0YkiVkFFlsZMrKGLfQ1dbzvfu0X5Tl0c5d38dXg/BbwHmF4vMRPomz5AluXpPfp+B/z7tGtPTjO//xlYxQUCLsvyQ8BDAMXFxfKmTZv+gsv/YLF3716U9zM37+XdyFKcwcHtyKZUZDlO8rJyVOZEAOJ+N4O9L2Es34CxeMPEeFkGWUYQZ5p0ZVmm99e3EWw5TMonH8Ra8dGJOZYsxHPkKWR/Fai1hDUBvNEAbZvu5qDaA3oNNyYuIFVn5MX+Zqo0QT6TW4RTf96UH3bp2eE+ija1iI8s34hDbyLDEeF31a/TovWQZnGwwd1ByCJyEANb88pZvGwi+Kzu6AmWqbKoiwWYv2oZP9z3FGkGK3dtvIqRcICnX/05h0whvr90Iw+c2UuwJ853NtxIsS156v7uSJBXG49yjXMFH8tbRMeJbeRbEnniWAv79X4qXcN8vmQN5eUzA96m0+IZZkdvExpRRKey8FHNAlSCgN6WzBeySlnpzP6rm8mV79TFUd7NX4f38298JVAoCEKeIAha4Hbg5ekDBEGYXpvwRqBh2rUJgiCcawt0Oe/Ad66g8Ncm5u5HCgcwFG+AeIxQ58mpc6HOaohH0WWdD8wSBGGWeAMEGt4k2HwIlcWJ68m/x3dmBwAqow3buk8h6MzEvSOEOk9SV7iRPYKGYnMi9+ctJt9k56meRramF+GJRvhV20l6At7zc7ceoUZjJju1EMekj9met5TLNSqCY70s9LrwDHfycEoJQSnOzTkLZqxtQ8o8To8NcGq0n70DrdyWtwiVKJJsMHNtZinPddTiCnr576ZKrs0smSHesiyzvecsRrWG67PKUIsqUgxmAvEYK505PNtRiyTL3J63+KLvWJZl9g20oRVVZJsSsGn1rE3J4RP5S/nmos2sTs5VfNwKH0ret7/1sizHgC8CbzAhzM/KslwnCML3BUG4cXLYlwVBqBMEoRb4MhNmciZ93f8I7BYE4TQT5vjfvV9rVVB4t0RcrSBL6DPKUCdkEmivQpZlZFkm0HYCdUImKqP9LeeQ4zGGX/4B6sQs0u75PbqMBQw8cj+B5sMAiDoj9nV3oU0voS3o5WDxZRRaErgmJQ+DSs0rA638tqOW9oCHO7PKiMkSD7ZVs3+om9BQB43D3YxaU1k+LahM0BqZnzkfu2+Q/xpoZw0mftnfzjJHJsuSZjYa2ZSaD8BXj78EwO3zzovt3QXL8ETDfHL/04xHQ3y5dN2Ma+vGBmj3jnBNZglW7UTFuHSjjaGQj2smfeernNnkWRIv+n76AuMMBr1cmVHMPUUr+FzJWv5hwWXclLMQo/riKWQKCh903tdKbLIsbwO2XXDs/037/28A37jItTuBi+eUKCj8DRAZakVlTEDUmdBlLsRft4PY+ACyFCfm7p1qNPJWeI4/S2TgLLb19+A/8wZpn/oNHb+/hwde+THO8mu4unwLecl59Bx/jp1ZFdjSS7kuNX8qOKveOwxAtdvFhqRM7slZyLaBNl5or6F6oAHBkIjZmUORzTnjvs/o7Pw7enLVKu4vXME1maUsSkxHvCDoa2FCKglaA43jQ6xPySPLtZvqFwAAIABJREFUdP4XkuVJWZTakql3u7g8rYCFieeNaiMhP6/3nqXMnjKjWlqGycaRwU6uzyrDdHoP9xZe3HQOcHSoG5vWwFKlbrmCwgwUu5OCwrtElmWig22oEydqc+vSJxp5hDqqCLVXISAgqrV0PXAVEVfLnHNI4QAjrz+ALncposEKoorIcAfdt/2UEYuTjtpt/Pypf+Jnv72HF/xepOzFbM0oQTstv7reMxFKUu12AWBQqbnJ5uAqVz29Ki0daSUscebOEOam8SG+31rNxoRU9t/wFb656AqWODJmiTeAShRZPynAd1wQKS4IAp8uWokA/H3Z+cjzqBTnhY7TWDU6PpFfMcPEnT6ZFqcSBOo/+jWuy5ooANPjd7N/oA1JPh/r6o2GafYMsSo5B+1btPtUUPgwonwjFBTeJXHPIPGQF8NkHraoNaBJyiXYUQ2yhCYln+FXfki45wye48+SdMM3Z80xtue3xD0uHDd8i6irGbUtFU/nSfZlVpC78dPcKMrU1O/hhKuNoCWFW5Zci117vnlJRIrTPFnq9OT4IJIsI4R9xE88z3ydgfy1n+J0OMhy53mzeCge4/NHXsSs1vKf6+9A9Q5Ki348fyneaJirL6hfDpPpWim5ZE7bmb/ec5bxaIjPl6yZMp2fI9VoQSWIuIK+Gd3F9vS30uMfJxSPcuXkfU4MdaMWxDnzvRUUPuwoAq6g8C6JDLaCFEfjOO9b1mWV46t+CZAR1AYCjXsQtAZ8ta/huP4bM3KS4/5Rxt78NeZF1yKIKlQGG5alH2Hf7v/C7R7gI6XrMRjMrM4oY5kk4Y9FZog3QKvfTUSWWO/I5MBID2f7zpLftBcEAd36uzHa09h4wbp/eOpNGsYHeXT97SQbzHM+myzLHHC1I8TDAKxPyZvahQO81HmGsBSjPCGdAmvSlHiPR0KcGRugbqyfG7LnU3iB2R7ArNZh1eoYCJ4PtAvGovT4x0k3Wqka7sGuNbLEkU7NaB/lienYdYZ38pEoKHyoUARcQeFdEhlsRdRbEXRmgm2V6HMr0KUU4J+sWDa297donPOwb7iPoRe+RaS/AV36+WrCnsrnkSMBErZ8Gf/pNzAUrEJOKeC4xkyef5jUaeKqEcVZ4g3nzeefzCzlwEgPJ87soMDmQLfydlT2tFnj9/a38vumY9xTsJwr0gsv+mxNnmEOuNqxhHysv+DccMhPnduFRaPjz94zqAUVmSYbwyE/nmgIlSCwODHjovMLgkC60cpQ6HyBxWbPEKIAdxUs483+Fnb3NdMbGCcixdgwrUWpgoLCeRQfuILCu0CWZSKT/m/viefp+cVN9P32k8QDboxlVyCFg8SG23He9F3Mi68HQcRX89qM64cPP8XBkiuojUSIxaLoMss5NNiJ35LMKv8gsn92F7ALqfeOoBNF1nccIQGZWnMqhs1fQJUwd0OQn9TtI8+cyLcWbb7onJIss6evBYNKTV80MFXZ7By1o33oVWq+uWgzXy5dx0pnNlEpRqk9mY/PW8o3yjdzb9GKt0ztyjDaGAkHpvzdDeODOPVm0o1Wbp+3mGKbk7qxAXLMiW8Zoa6g8GFGEXAFhXeB5B8j7h9F48jGf+p1RIONYNsxun58JbHRbjxHn8ZYsglj2WbUliQM+SvxnTqfkBFqr6Q25KM2dwVPtVbxO0FPtaBh70ALBVnzSVVriXXXTo2PuweIdVQieQYnisEAcixCnauVIimKOuxjaUIqNYIK4SIdt3oD41SP9HJb3qK3bOxxarQPdzTItZmlyECde2DqnCzLNLgHKbWnYNHoKbA5uW3eYv5v+WbuLFjGmpRcUo3Wty1fmm60EZMk3JEgkXicTp+bhQlpCIKARlRxb9EKKhyZXJtZopRCVVC4CIqAKyjMgSzLjFe+QNg1u5UmQGSoDaQ4amsygaYDWJbdTNZXX0VlsDHw6OeQIn6cN31vSnzMi64jMtBEZKAZgOHDT1CdmEd6djk3hkZRWZ083VGLOxJiU2YZ6swFSL11xEe7iVQ+T+zok8SbDxM9/DiRvQ8RPfU64f0P0xDys8CahPGqr7I8s4wW7whj4eCcaz7Xg/vayajvcDzG/v42Bqf5oqNSnP2udgqtSVyWVoBDpefUaP/U+R6/G280TIUj8z2933SjFVEAV9BHm3cESZJYmHDe5G9Ua7mveCUl9pT3dB8FhQ8yig9cQWEOoiPdBM7uJ+YZRJfyuVnnI65WBK2BUF89cjSEaf4WdOmlZP3D64xs+w80CZloU8/7gM0Lr2bohW/jO/UaNuvdHG09gb/sSm7QGkmNhyktXUeXNZVwPEqK0UI8fyXRrhpix59FNNrRLrkRVeYCpOFO4n31xF3NDFqSGR8dYWHhKgS9mYqkCVGtHulh8xz+59e6Gyi1JZNvcRCIRXi6rYahkI8jQ51sSS9kiSODquEegrEo12eWIQgCBTobjUEvIyE/Dr2J2tF+zBotZe9RWJ16E1pRzUDQw3gkhF1nINeS8PYXKigoTKEIuILCHIQ6TyLHY0QGmoj73aimpUjJkkTE1YQmIZNA3S5EnRlDwSpgIpXMedN3Z82ntqehz63AV7uNmM5CtdlJVs4S0se7kbUm1KmF5E+rKiYm5aIpXI9osqOetxxh8pxosqPOWYQsy7T2NcHBZ5lvTwVgcWI6KkHgxBwCPhD0UjnczT8s2Mh4JMRTrdWEpRj3FC6neriXN3qbaPON0uVzU56QRp7VAUC+1kKHKkDtaD8bUudxdnyI5c6s95yTfa6kan/AS3/Aw4r/hVrmCgqXOso3RkHhAmRJItRVgyYhA+JRQh1VM86H+xqIeYbQZizAX7cLY8lGRPXcfufpmBddS7i3jv3HnyNgz2BD/nIkVzPq1KIpgT6HIAjollyHpmjtrHPnzte5XQhA6WTtcaN6YmdcNdwza/z2nkZkYI0zl0daKpGR+ULJWpY4Mrm3aAVb88rp9I0Rk+Jck1kydZ1BVFNqT6He7aLFM0xUjrPsPZrPz5FutNHpHyMixSlPTP2LzKmg8GFCEXAFhQuIDLUT941gyF81o775OfyNexH1FuRYiLjHhWn+lrecT4pM+KTN5dcREUROiHpyMheQOt7HG5EIYylF72qddWMD5FkSMWnOC3yFI5OTo73EJGnG2Fd7Gii0OKgd68eg0vCl0vXkTEZ3C4LAxtR8/s/8DdxbuIJ0k23GtcuTsvBGw+zub8ahMzJvcnf+XkkzWIlLEiaNlgLr7HxxBQWFt0YRcAWFCwh1ViGo1GhT8tFllxNz9xId6wUgMtpDZKAJw7zlBOp3gyBiKrt4Slag9Rgj239KqKsWjSOLhvy1BLUG1pVv4dWGA3wZAx+tP0yrd2TGdaPhAD889Sa/PXuUXv/4nHPXuV1T5vNzLEvKJBCL0jg+OHVsKOTj2FAXa5JzGQn5uTFrPilGy6z5Mkx2yh2z089K7SlYtTrGwkGWOjL/YqbuDJMVtSBSaktGM600rIKCwjtDEXAFhWnI8RjhrlNoUgoRVBp0aSUICITaTwAQaNyHIKrQZS/CX7cTfd6yqf7fFxLqqiVQvxuV0Y6/biej3hGqlm4lb/ENJHoGeSAaJ89gxh+L8JFdf6ByuBtZlnmlu55Nr/+GXzUc4vs1O1nx6i+4Ydd/81jLeUvAeCREl9/N/AuCyc5Fh1eNnDejv95zFkmWMal1ZJisLJ5DpN8KjahicWIGOpX6PUefTyfDaMOuNbDCmfX2gxUUFGahBLEpKEwjPNBMPDiOceFVAIgaPZqUAkId1ZiKNxDqqEaXvZi4b4RwzxkcN3yLmHcYX80raFOL0ecsQdQaCPefxVe7DX32YixLb2Tk9Qd48cRLRBx5XJVVxuMHH6cHkSeX30CuJZFP7nua2/c+wfKkLA642ilPSOOPmz6JQaXh1e56Xu6u5xtV2+jyj/Gt8s00TDYumZ8wcweeZbLj1Jt4orWaRJ2RzWkFvNbTQJbJjijAlvTid7WDviqjmFxzAmlG63t/yZOYNDr+eelVSp63gsK7RBFwhQ8tUjTM+MFH0LhCyNIGBFEk1FmNoNGjTcrFe/Jl9NlL0GUtwlv5LO7DTyDHIxjmraDtxIv4VRpy5m8h0ldPzDtEzDtEsOkgmpR8IgPNaFOLsK+7C0Gtpa9wA2dq3mCtI4to72l+HRfYmJjGprQCAF6+4h7uPvAMx4e6+Fb5Zj5TvAq1OCG0XypbxxdL1/Lt6u38pvEIJrUWq2airOqFO3BBEPinBZfx76f3cP/hF9Cr1ITjE+VI041WlvwPd9/nsGr1M/qJ/6VQxFtB4d2jCLjCh5ZwXwOhnjNoXGOM7HwQ67KbCfecRptSRKi9koFHP4c2pZDMr76GoDEQGWpHm1oMejP/3V6LLW8t5cn5BNuPo3HkYF/zCQJNhwi2HUeTlEvCpr9DUGuJSnG2CToSTIks6azmx5KIFxXfWX7D1FoSdUZevPxTeKIhEufoDjYeDXF9Zhm+aJgHzuwjzWAlSWciWT+7GcnH85dwW94ijg9381p3A0eGOsk22d/17ltBQeFvE0XAFT60hLtrEbVGQnnlxMaaGX3jZ8jxKNrMBQw+9RVEvZWIq5mR136IsXgDwbbjGPJXUnfgMYKj3QRzl3PS7SLX3Y+xeANqazLWZR/Fsvh6EASEyVzpXb1N9Ie8fHz5jfQdfJQnZJFbMwopvWD3rBbFOcUb4MhgJ1XDPXyuZA2BeJRtPY1sTJ130R2sShRZnZzDKmc2f2iuRERgiSPjL/sCFRQU/ldRBFzhQ4kcixDua0CTWkQ8lk3C/MvwnXkDKeQhOthKqO04zq3/RnSoHfe+32EsXIt15R1E+hqo2vt7xNwVOIrX8VrXae6LRrE5z3fM6g75qHe7GA0HGAn5afeNssCeSm5qAffbc1GPDfC1pdfOWtNZ9yBtvtEZedjnaPOOoldpOD7Uxa9W3cy3q7ezPvXte2Q3eYZxBX18smApKlHZfSsofJBQBFzhkkeWZaSQF5XhnQdYhfubkML+ifaeXT5EnRFrxUeRJYnun16DOjEL26o7kGWJQNNBBp/9Oikf/xl9j95PV8FlZC67hY0p+Tx2eheHUXGbMxeAquEenmqrJiLFsWh02DR65ieksik1n6rhHl4Z6eMrZetnBYOF4zG29TQSjEdZkZSFQ2+aOjcaDjAeCVJkddLkGWIo5OU/ll/39s8Yj/FG71nmWRJZ5lAivRUUPmgov5IrXPKE+xoYeulfiQx3vuNrQt2nEDR6NIkzhc13attEdPnV/4Cg1iJq9KTe9UukkJe+h+7EY0tnfMlH8ADuWJiyWIjjGj1uQcWbfc081nKC0XCA8oRUvli6ljsLKrgmswS9Ss33a3aSrDfz+ZI1s9Zz2NVBVIqjFVXUjblmnGv1jKAWRG6ft5hEnZGDgx3v6Bn39rcSjse4NXeRsvtWUPgAonyrFS55wj2nkcM+/HW73tF4OR6bCFZLLkCYJmxyPMbIth+jTSnEsuxmOv3jhOIxdGklOG/5FzRJubhv/RExjZ5ftFbxnfqDrA2NIeht/KrhMH/uOkO+xcGfOk/z2cMvsK2nYWrubT2NnBjp4WsLNs2onAbgiYQ4PtzNyuQcCqxJNEwrwgLQ7BkmzWgh2WBhfUoerZ4RhkP+t3zGPv9E69BNqflkmu1vOVZBQeHSRBFwhUsaWZYJ958FlYZQz2kiY31ve014oBkp7EObMX/GcW/Vn4gOtuC49p+o847yi9ZqnupuQJZlbKs/Qe63D9GsMqJVqfDFY5xwuxiJBFmTVoAr6GV5UjYGtZrxaJhUg5UvHvkTx4a6iMTj/Nup3ZTYnNyWt2jWevb0t6BTqbg2s4RFielTvnOYaO/ZExinxDYR8LY2JQ+zRsdhV8dFn0+SZV7racSpN3H1HP50BQWFDwaKgCtc0sQ9g8R9I5hKL0cQVRPlTachSxJhVwtyPDZ1LNxzCkGtRZuUM2Os++AjaFOL8RVt4OmeRlSiyOnxIeo8wwAEYlFaA240wvmyny8JWlblLubzpWvYnF7Ai51ncOpNvH7lfWSa7Nxz4Bm+V7ODDt8Y3150xSxTdn/AQ53bxWWpBdi0BsoT09CIInWThVq6fW7ikjTVvtOo1rLGmUP9uAtPJDT7fUgS+wdaGQ37uSV3Ibr32DVMQUHhb5e3FXBBEL4oCILSqFfhb5JwXyNIcbTpJehzKgh1VBPzDAET4u05/hyjO37ByPafERntRZbihLvPoEkuQJhWfzvc10C4qwbtytt5rLueuCxxb/ZCEnV6XupvIRyPcdY3SkSKE47HAZiv0fCSoEWwJJGgMzAaDrC7v5mbshfg1Jt5cuPH0avUPNpygg0p89iUmj9j7bIss6uvGYfOxOWT7T8tGv2EGd09YUZv8gxjVGvItZwv17oxLR+tqOLJ1pMcHGhnNBwgJklUD/fwm8YjHB3qYoUzhzK70uFLQeGDzDvZgacClYIgPCsIwtWCUjpJ4W+IyMBZVGYHKr0FQ/5KQMZfv3tCvCufJ9B0EF3WQuK+YUa3/5TxY88QD7rRpZXOmMdz7BlkUc2rugT6Ah5uTivCqtFyTXIeg+EAuwc7qfeMYFRp6Av5SNYZuUuI0y1D1chEo5NXuuuJShJbcxcCE2VNH99wB+tT8vj+0v/P3n2H13GViR//npnbm+pV75Yt995ix47TGySQEAgthMCyLMsuC9vYXbYvbF+WBX6wlKUGCCGBVEh3nMSOe5VkWb23K92r29vM+f0hRbZjOREQuZ7P89wnnpkzc89M9OjVOXPOe244Y852dzRIXyzEjeUNp7WUV+SXMZaKTU5Bi4yxIMd/2mIfOTYn98xbS7HTw66Rbr5+fBdfbNzBMwMnKHV5+cTCTbyvbpXKcqYol7g37V+TUn5OCPHXwA3Ah4GvCCF+CnxbStk+1xVUlLOR2TSp4VbsFZMBU7M5sVevItGxF2lkSLTvwdVwJa4FWzDTCaJHnyLc9AISia3olDnURobx/Q/zUv1Wjg23crXDTVFRFfc2v8z7KhexzGLluSPPYEnHqK9dw9OxIPNdPq4fH+bv9Bwe7DrCOn8lD3UdpcHnP22FsCV5Jfxk2wdmrP8rI10UOtxnLOaxPL+Uh7qOsHOkm2A6wU1T632fVqagjOUFZUykExwZH2QgHmZtYQV13gIVuBXlMjGrd+BycgmkoalPFsgDfiaE+Lc5rJuivKHUaCcyk8RWVD+9zzlv43Twds7fjGvBFmAyuHuW3cgvehr5Ym8LT3UeIjn1XjzRt4uf+io4mlvOxsIqVlp0du/+KdsDvTzUuJ1NJ17AmoqSEjo1bTtpjY6zwGLFLQQ3FdXweG8TLRMj7B/r486aZbMKoIPxMN3RINtK67G8binN17rRj4wPTC63+bqMbafKsTnZUlLHe+pWMs9XqIK3olxG3rQFLoT4Q+BDQAD4FvCnUsqMEEIDWoE/m9sqKsrM0gPHEZoFa/7JJS51hxf30htBGjhr155WvvPnf8cJKfHIDL/c8V12JROszS/jubEmHM4c3uF0sWjN29A8hTz7/HcgNMr+dJqcFW/jpsIado10Yek9SHIizLzoCMLq4F316/j50AN8avcjCOCd1ctmVfedI93kWB1sKJx5gZAV+WU0BocpdnrPml5VUZTL22yGqBYCd0gpT8uSIaU0hRBvm5tqKcpJ0jQmp37FQzjr1k0PPksNHseSXzGdc/w1zppVZ1wjevhJdjdtx1xxG3eVzSPy8N/wam4Jz1Yspyxwgpty/eTmFKHlliGBp1JJ7JpOwDQYqFzKEk8+S/xVPOXJg1d/wfzYGFrpfLaU1lPs8HA0OMTmohrKZrHc5ngqzonwKDeXL8Rhsc5YZnl+Kb/oPjY9+lxRFOX1ZhPAnwTGX9sQQniBxVLK3VLK5rOfpii/nWwkQLztVZJd+zCiYyAh2XuY3M0fQmYSZCcGcS+8+k2vkwn2M/jAn3K8djOVi66lsGYp3o493Lbj64QXXoNn6BjW6vdiqZjs/t4f6GMoEeGTizbxlead7BntpcYzOQq8JTYBwML6DVhLG9A1jTtqlvG147u4s2b5rO5r10g3Tt3K1pK6s5bxWh38/qLNFDu9s7qmoiiXn9m8A/8aED1lOza1T1HmjJlOMvarLxI79jS6Kw/vmjvxrLyVVH8j489+hUTHXjAMbEXziJ94ifa/WMzAt+4jcuARzFQcACM+QaJzL0M/+CTdVhfhpTfjtTl4sL8FcfOfI4rn4zv+PNm8eoQrB71iKQBP9DZj03Q+sXAzOVYH+wK90/U6MTFKuSuHwnV3YKmYTATzkfnr+XD9Om6rXPym9xXNpDgaHGKjvwqP1f6GZWu8+TjP0kJXFEWZTQtcTA1iA6a7zlV2CGVOpfqPYSbC+Dbfgy3/5DKYmsNHZN/PiIYG0ZxehCuXwOP/itAtJHsPEzv2FMLmRHfmkJ0YmjxJCDpv/yec3gK+39vE3tAQX3Pn8Fe3fo6ND32WVPFSPL4itNxSpJQ82dfMlUW17BrpZnFuMXtOCeAt4VEacvyn1bXU5eOf1tz0pvdkSsnzg+1YhGBbaf2bllcURXkjswnEHVMD2V5rdX8C6Ji7KikKJLsOoLlysOaVIaUk3vwCqcHj5F39u/g2f4jo3p9hK1tEouVFUj0HKXrPv+Hb8F6SnXuIHHxsMlVqSQP2kgUkiuZxYqibJZ58vtxxkC0F5fQnonyk4whbrvljPtN5mOKK5QghODw+QF98gt9ZsIH9gV7y7S52jXYznorjszpoCwe46g26vs8mbRj8oucYHZExbixfqAamKYryW5tNAP848D/A5wAJPAd8bC4rpVzejESE1MBxHNUriOz5KcEX/pf0UAsAMpOk4KbPkHvNx5FS0v+l27HkleNbdxdC03DO24hz3sbpa0kpebnrEBkji0UTpE2De6qWcFVhJT/sbeK/TuzmE958Hi6soQp4sq8Zi9CocOcwlorjsUwuPLIv0EedN5+0abDA55+p2gBkTZPdo91EMmlqPHlUunMxpMkDnYcJpRPcXbuKTcU1c/n4FEW5TMwmkcsIcPc5qIuiAJDsPoSZTTH66OfJDLdiK11E8fv+m/iJHYw/9V84523ANX8ziRMvkew+gP+uf0ZYbDNeK9b6Cjsbd1DhK6JRtyKAdbkl2DSd+6qXsbz/CPeGDd6970kevPoeHu89zqaiGoYTUVwWK3l2JxYh2BfoJWNOplBtyPGTyGYATntH3RMN8kRvM+FMCqfFyqGxfkwkuqbh1m383sIrWDBDUhZFUZTfxGzmgTuAjwBLAMdr+6WU981hvZRLnMxmMNNxZDaN7j09AUmi+wAIyAy3UvC2vyDv2t9HCIFnxS0kew4z9INPUvWnzzD21Bex5Jbi2/CeGb8jNdTK0eMvM+HM4crIEF9IJmnw5JNrcyBNA6N7P8siw/yHtYo/T8e59ZlvM5aK85H56xmIh7m2bD4vDrVT6ylgT6AXp2XyD4B53gK+fWIPkUyKPJuTUpcXCTSFhil3+binfi113gIGE2HaI2OMJKJcVVJHkRpRrijKW2g2Xeg/AI4DNwL/ALwfUNPHlN9IrOkFoseewsymYapF61qwBd+6OxFCkI2OkQl0wlSWNM/KtyGEYDyVYF9olE33/D+GvnQ7/V95F+nhVvx3fh7NcuZo7mwkQPTgI+x3+/FVr6ReFxw8/AJ36DrGeB/ZpmchHsRauYI6s5qfLK7jvS/+EE0I5vsKGE1GWFdYSVdknGKnhz2BXvJsTqrceQwnokSzKbaW1BHNpOiKjhPPZrilYhHXly+Yzlte4c6lwq3W4lYUZW7MJoDXSynvEkLcLqX8nhDiR8BTc10x5dIjpSR24iWEzYmrZg3C5iIbHibesgPdnYdnybUkuw6AaZCdGET3+rEWTC75uWOslxdGe+nx+XnHO/6O4IOfRfcV49t45tsdM50gvPsBxuweuooXsMVfRatuJc521qZCZPc8gOYtxHblh9BLF8Lu/SzPL+WRaz9MVzRIIBUjz+aizOVjQY6fHJuTtGnwwlAbV5fUc3h8gFybkzuql6FrGlJKDGmekRJVURRlLs0mgGem/hsSQixlMh96zZzVSLlkZcb7MKJjeFbcimNqzrWUEplJETn4KLo7l0TXASz5lUQOPoqjdh1CCKSUNEXG8FpsHAuPopWt5OZrPomnegWa1XHad0gpiRx4BGmkaGy4GZGIsCq/nO+17QOgYtHVHEmE2bjyZjTb6efW+wqp8xbwxWM7WO+vQghBQ46fEodnsv6mSZ03n7ZIgKtL6qfX9hZCYBEqeCuKcm7NJpHLN6bWA/8c8CjQBPzrnNZKuSSl+o4CElvRyXWxhRB4V74Na34lEzvvJxsawJJbSnasZzqX+Wg6QSCZYHNBOTcW1XJ4YphfaFaCw+2MRoP0JyIMJqNIKUl2HSAT6MK26nYOpJMs9BXhtFh5dbSbWk8e+3UHL9pzeHyoA/NkeoNp/bEQadNgSd5kCtNqTx6FDjfFU0HcbbEhJawtrDjjXEVRlHPpDVvgUwuWhKWUQWAH8GtNgBVC3AR8CdCBb0kp/+V1x+8F/h3on9r1FSnlt0457mPyffvPpZSf/HW+W7nwJHuPYc2rQLM5T9svNB3v2juYeOX7mPEQZnwyXamzbj0AzeEABpJ57lw8FhtmLMjT6TQnsgby5R9DaQOaEFzjK2Rj03M4KpfTmFNObDzAen8lppTsGe1lc1EN0WyKtYWVHBjrI2lkeGf10tPqciIcwKFbqPcWAmDRdOp9fkqcXoaTUZKGQY0nlzJXzjl4YoqiKGf3hgF8KuvaJ4Gf/roXFkLowFeB64E+YK8Q4lEpZdPrij7wBsH5H4EXf93vVi482UiAbKgf9+JrZzyuWR30Lb+Vo2MDXHf0MYTVgX2qm70pPIbf7pqek70sE8GtSaK1q9A79+DMxmnPr+aZxhcI6g4+sO5dvNzDtcj8AAAgAElEQVS+n1KXlxKXj6bQMBOZJF6rgyKHhw/Wr2a+r5AHuw7z4/ZDLDaz0/VoC49R7ys8bZGRhhw/NZ58NCEwpDndva4oinI+zeYd+DNCiD8BHmAyDzoAUsrxs58CwHqgTUrZASCE+AlwO5Nd8G9KCLEGKAZ+Bax9k+LKBS7VdwxMA1tpw1nL7AyNcjweobivmYaqVQjdSsLI0BGfYNUpq3KZY73U5hTiXPcO0hiEOvawMzjEmkyUA0XLiXYdZSAe5raqyVzlu0d7ALDrOpuLa9GExqbiGlwWK/e3H+Ch8ACjfbksyythLBXjurL5p9VrQY6fak8ey/JKSBgZVheo7nNFUc6/2QTw1+Z7//4p+yRv3p1eDvSest0HbJih3J1CiK3ACeDTUsreqa77/wQ+CMzcZAOEEB9jKiuc3+9n+/btb1Kly1c0Gj2vz8dx/DGQFo439cx4PC0N9kx0YRoZdibj5BUsoutAIx3pCIFYEEvMTctwJ0iJt+042dxakrv3g1HOExknX4pG+EeZy3yjgD3Hj+HSLGRjPTSKXp4aPUSOZoVAmGS2i+0tfdPfe6VhZ6+0sePYIV6Q5uSUtXgb24+frKeUkuREgICRocbmZd8ru+b8eV0ozvfPzYVMPZuzU8/m3JhNJrba3/DaM/Uxvn7U0GPAj6WUKSHEx4HvAdcwmW/9yalg/kZ1+wbwDYCGhga5bdu237Cql77t27dzvp6PkYgw0v8IrvpNuBYsmd4vTZPokSdwzb+S5mwWb9coyxMhjticsHobV65aQl9vM8UTaTbVLUQTAhkJkO6z4di4DUv1CgD+JdUKo928UlbFt7dez+rwGDZNp9KTOzmC/RcvUubN4+aV67m57sy1wnO2b2fVpg28ONhO2jS4rWb5GV3kwx257Bzp4u4FG1iWXza3D+wCcj5/bi506tmcnXo258ZsMrHdM9N+KeX33+TUPqDylO0KYOB11xg7ZfObnBzdfgWwRQjxCcAD2IQQUSnlZ9+svsqFJ9V/DIwstrJFp+0ff+qLk6lRF2yh8W1/i02zsDXQTmc2xSv2PFZKSXNknBqXD20qoBrjPaDraP7J+eEpI8uu8QGsmsbzwx0EUwnm+Qqmv6NlYpSxdJwVBWVsLqo5ax1zbE5ue92AtlNt8FeRMDIsOqUrX1EU5XyazTSydad8tgB/B9w2i/P2AvOFELVCCBuT+dQfPbWAEKL0lM3bmMrwJqV8v5SySkpZA/wJ8H0VvC8e6UAPia79GLEQMPn+W3florvzp8tEDj3O+FP/ha1sEfETL3HsyC+pdeWgde9nra7RkUnxYqCXcCbFAk/e9HnmWA+apxDNNZnhbF+gj4SR4Q8XXUnaNHi89/QhFl8/vguL0NhWPO+3yopW6y3gw/PXq2QtiqJcMGbThf4Hp24LIXKYTK/6Zudlp0awP8XkNLL/k1I2CiH+AdgnpXwU+EMhxG1AFhgH7v31b0G5kEgpmdh1P9lgP+gWLN4ijNg49qqT3dKpvmMM/+iPcNSsofyTD3Loh58h0v4q8yoWY/QcYvGKW9ljsfLUcCcIqJmasiWlRIYGsFQun/6+F4fasQiNjzVs5LHeJh7uPsoH69cA0B0N8lD3EZbmlXJzxUI1clxRlEvKbFrgrxcH5r9pKUBK+aSUcoGUcp6U8vNT+/5mKngjpfwLKeUSKeUKKeXVUsrjM1zju2oO+MXDiI6RnRjCWb8J98Kr0exuNKsTe8Xk++psZJSBb30Y3ZVL6X3fQrPYGb3m98DmpvSnfwapKLbq1WzMKyVpZCl1uHHoU39nxoKQTqCfkgjmxaEO1hZW4LHauaN6GXsCvfREgwB8pfkVNKGxvrCShtyzLwGqKIpyMXrTAC6EeEwI8ejU53GgBXhk7qumXIxS/c0gTRzVq3DWrcO3/i7yrv09rFNraAce/TxGbJzSj3wHi29yac2WdIrSlbfgSE52ues1a1ieU4Tf7mbl1PKbw8kY97fvRWo6WuHk++/RZJRjoSGuKpkM6HdULwPg4e5j9MVCPNh1mCv81VR78vC+LuWqoijKxW4208j+45R/Z4FuKWXf2Qorl7f0YDO6Ox/9LJnKEm07cS+5DkflZLCNZNL0xMNsqFyO7ZbPYrS8iMirwCoEHz5lUNn/dBzg/qFuHHYfd0+9/35pqBOAK/xV7BrpZn1hJVf4q3m4+yhDiQgAy/NLqT7lHbqiKMqlYjZd6D3Abinli1LKV4AxIUTNnNZKuSiZmRSp4VasxfNmPJ6dGCIb7MdRczIvT0t0nIw0WeDJw7r5Hhz3ffuMd9VJI8tjg+0A/EtWEsmkANg+1E6+3UXSMHh24ARd0XHurFlGe2SM+zsOTLfIa735KIqiXGpmE8AfBMxTto2pfYpymvRIOzKTxFa8YMbjia79xHQrg6WLiGTSSClpnlplrPB1+dFP9fxoD+Fsms/IFGOGwX8cexFTSnYMd7C1uJaWiVHsmoUT4QC3VCzCruloCG6vXIKGoMajAriiKJee2XShW6SU6dc2pJTpqWlhinKa1EATQrdhzSuf8Xiicx9PlixlOJ7A0vwKXouNhJllgTvvDUeIPzRwgmLdwkfNOCOVS/hO216W5ZUwmoyxprCSoXgYn9VBR2ScmysW8slFm9E1jYw0cegWSpzeubplRVGU82Y2LfDRqaleAAghbgcCc1cl5WIkpSQ90Iy1oBpxlrnSTX1NjBTWsqmwkqsKK6lweim0OVn9BslRRlNxto/2cls2hqN6JX++6iZyrA7+dN/jABQ53FiFztaSWibSCYKpBJ9ZehWfWryFgdgEle5cNXdbUZRL0mwC+MeBvxRC9AgheoA/B353bqulXGyM8AjZSABbycwzDI1MkpcTcXy+IjYVlLE2r4RbSup4f+Viih3us173kZ5jGEjuyPVjW/NO8h0u/mrFtWRMk4U5foYTUWq9+WzwV2MRGm3hyb8tTSkZTkbVADZFUS5Zs0nk0g5sFEJ4ACGljMx9tZTzwUxGyYRHsBeduU6NlJLseB/ZyChGbBwjFsRWVIejejVCCFIDTZOrjRXNPIDtSNseAlY7N5bMQxezSz8gs2ke6jrMUgFLt9yDmFpO9D21K9kx1MHK/HKGExG2ltSRZ3dR6vLSFg6wzl/JaDJK1jSoUQPYFEW5RM0mF/oXgH+TUoamtvOAP5ZSfm6uK6ecO9I0Gd/xf2RGOym44VPY/DWnHY8ceoLYsadBShAgrHbiLS/h6DlCzvq7SPY3ofuK0ByeM68tJU93HSI3k2DJvJML0g0nY3zqyPPk2xyszi1mbW4JixxOrIkwMjZOY+8xmk3J39evQXOfbElrQvC1TXeya6SLl4a6WJ43mZF3YU4x24fayZomfbEJdKFR5VYtcEVRLk2zGcR2s5TyL1/bkFIGhRC3ACqAX0ISbTvJDLciNAsTux+g8KbPICxWAFIjHcSansVesRRX/SY0pw80nWTnXmLNL5AZ7cRMxbDXrJ7x2kfDowyO93OjmcaSM/m+O5bNcN+BX9ERnyDf6uDJ4ck53S4p2SqyXIfJPs2KVejcsXTbjNdtDo1Q680jz+4CYFFuEc8OtNIbC9IXC5Fnd5JjUwlcFEW5NM0mgOtCCLuUMgUghHAC9rmtlnIuGbEQkYOPYy2sxTlvA+FXf0y08Rm8K27BzKQIv/oTNLsHz9IbpruxAZx167H664gefAQzmsRe0nDGtU0peWq4i/yxLhYU1ZFtfYVMsI8/0Lw0R8b59uobudpfRX/vMfY1PservlKeTSX5VToJJtxUvoD8qQB9qol0kuFEhC0lJ7v7a70FuCxWWsNjDMTDzPMVqvzniqJcsmYTwH8IPCeE+M7U9oeZXLdbuQRIKQnvewiZTeFZcQu604e9agWxY89ir1xOou1VsqFBfJvvOS14v8biLSTnynsxYkEs3sIzrv3oYBt9wUFuHmpCX/gxjM69fEFaeZ4J/qFhI1f7q5CJMIUtL3BrURV3XvVRpKZzcKyfHcMdvK3y5BKkppTTy4o2h4bRhTbdfQ5g1XTqfYW0hEaIZtPUqAFsiqJcwmYziO3fhBBHgOsAAfwKqJ7riinnRrLnEMmew7iXXIfu9AHgXnwtmZF2Qju+gxEN4Jy3AVv+zHO7AYSmnxG8AZ4e6WL7aC/rE0HmxccRdiffTcT4AVY+LLLcPXIcWbGQzKFHERY79g13I3QLAlhTWEG5y0dreIyDY/0MJaLEMil0TcemaaRMg1pP/nT3+WsW5hRxeHwADUG1ev+tKMolbDYtcIAhJrOxvRvoBB6asxop54zMpons+zmWnBIc1SffX2tWB+5ltxDZ8wC6z4+rYesbX8c0SPUewV61crrL+qVAL78a6mR5jp8rWp7GsNh5PBzin6WNm8sb+OuqhWR3/4T0jm9BNoVj0wfRTlmveyKd5Ltt+7EIQbHTw/K8UvwON2nTJGlkSBlZ1hRUnFGXhblF6ELDommUu2fOx64oinIpOGsAF0IsAO4G3guMAQ8wOY3s6nNUN2WOJfubMGJBfJs+gNBOn9plL6mHde/CkluK0N/477zAY58n9ML/UnDLn5F/w6fYHxziFwNtzPfkcUNRDameQ+ytWc+fGYI1vgK+vPGd2C1WdCNDcu/DWBdswVKx5LRr7hzpwqbp/NWKa89oZb+RQrsbv8ON22LD9ib1VhRFuZi90W+448BLwNullG0AQohPn5NaKedEqucgmsONNf/MliyAvfTMQWmvF299hdD2b6B7/Yw9+W+E8qt40JFPmcPD20vnIbIpmidG+WTdNVRrGt+5+kM4p0a3R8oW8/0F42ytWMyqU64Zy6Q5Ghxkc1HtrxW8AYQQfGDe6ul35YqiKJeqN8qocSeTXecvCCG+KYS4lsl34MolwEwnSfY3YStd+BuP1DbiIYbv/xTWwlqqP/sCltp13L/9O8iJIW4vrYfBFo599xN8fOltuJF8f+FG8qeyrkkpebKvmbRu5ZnBEwRTienrvjrajYbgmtL636heNd4CqtQCJoqiXOLOGsCllD+XUr4HWAhsBz4NFAshviaEuOEc1U+ZI6mBRmQ6ib18yVnLBF/8FrHGZ896fORnf0U2PErJB7+M7s7j2Nv/loCnkOtf+Ar645/nqR/9MXeXr8a0ufi2VVDVsGn63MbgED3REDdXLMRjsfFoTyOmlCSNLAfHB1hdWEHBG6RYVRRFudzNZhR6DLgfuF8IkQ/cBXwWeHqO66bMoWT3ITSHF0tu2YzHE+27Cfz8bwHQl9xA+IZPE3LlUufOpdzmILr3QaIHfkH+zX+Co2olbdEg26MhVlxxN1U/+gO+OdDKfy69ncXuXL4aH6Kqdj3CNtkdnshmeHqglQU5hVxXNh+/3cN32vawe7QbU0pM0+Takt+s9a0oinK5+LVG+Ugpx4H/nfooFykzFSc10Iy9avlZu8/Hn/0Kzf75tK+8nZ72PchHPo8smo9IhLFPDFAZG6Og4Vrylr0dbbiT3cEhvBYb11av4b/e8QW+NtzNLcW1fCEVwJWxYpl/svX97GArEsldNSvQhMbKgjLWhip5cagTq6azPL+UUjWCXFEU5Q2pYbqXoWR/IzKTwl42c/d5qu8Y/W27eGHLx8mr28C6eRuoevUHeA/+jIHyZXSXL6Ynr5L2/GrkaA8ADt3Ce8obsAjBg8ERrvNX898WEzkyjP2K96H7igDoiQY5Oj7IrRWLKHFNzjsXQnBn9TLaw2OE0gmuLZ15RTNFURTlJBXAL0OpnkNoTh+WnJIZj48/91Wa8qqR5Ut4b8Ui3BYrVP87Ukr8QrDiDa7dFZsgkE6wTQfZ34h9yfVYKpcBYJgmv+xroczp47ryBaed57bauW/BOrqiQarVCmKKoihvanbrOiqXDDMVIzXYgq180Yzd5+nRDkKHnqBt0XXU5xZPBu8psxmtvi80DMDKoWaslSuwLLl2+tiBsX6C6TjvqF6KVdPPOLfak89VJTMvR6ooiqKcTgXwy0yy9ygyk8RRvnTG48HnvkaXt4ho+VJWSAMjOICUclbXltk0ezv345OSBfnl2NbdOR30E9kMO4Y6WJJbwqLc4rfsfhRFUS5Xqgv9MpMaaEJz5qB7/Wccy4YGCe99kNaNH8Lj9OE/9kuyUiIcHkTxfPTyZWi+M88DMIbbyDY9y/40rPLk4tr2UYT15KJ1Lwy2gYB3Vi9VK4QpiqK8BVQL/DIipSQT6MKaXzljEA2+8HVCupWuqtXM1wTX4uL3vZWkC2uRA81kXv0hMhUlZRr8T/sBXgr0AWAEOske/AURTwFtaGyoW42wnlyHezge4XBwkKtK5lHk9J6z+1UURbmUqQB+CYq37ybRue+M/WY8hBGfwDJD6tTsxBATO39I56o7MB1eWoODjKLxXDTE++MpolvuBaCrv5l37X6E/2rbx98efwUpJebAcTRnDo2LrwdgXWHl9HWllDzVf4I8m5Pryxac8b2KoijKb0YF8EuMmYwS2fcQ0SO/POPddTrQDaY5Y+7z8We/StbI0rzgKmpcPn4cHGK1Bv935btpCY/yjj1P8GNbDre1HaI7HubOsgV0xCY4EBrGDHSilzSwd6wfi9BYdcrSo43BIfrjE9xSsWg6B7qiKIry21MB/CIlpZxxcFm89RXMZIxsJIARGz/tWCbQjdAt6J7T1+7OhgYJ77qfkfV3E7Q4SJpZeg2DD+cUckN5Az+7+h5i2TR/l8pSLQ0e33A7f79oE07dwoNdhyGdQC9tYF+gl6V5JdOBOp5N88xUxrX1/koURVGUt44K4Bep4PZvEd738Gn7ZDZNvOVl9JwSME3SQydOO54e60b3FZ+xdOj4s19BmgbHFt+I22LlicEOyjC5qXRySteqgnIev+4+/rF+NT8mTnkqgsdi45biOh4b7SWhWzH9NRwaH2DtKd3nT/W3APDumpVoQv2oKYqivJXUb9WLkDQN0kMniLfsINF9aHp/omMPRjyIe9lNaA4v6eHWkyeZBtnxXiz5p+c+zwT7Ce/6EfH1d9OGoNDmZHdoiA/INLa8k13hVZ48PrzieuxWB+ZIGwDvLm8gJk2ecRfRGI+QNLKsLZzsnm8LB2gOjXBjRQPFLjVwTVEU5a2mAvhFyIiOIY00aBrhPQ9ixEJI0yB2fDuWvHJs+eVYC6pID7dNd7NriXFkJoU17/Su7OAzX0YiObbi7ehCsGt8AJemcZcw0XJLTysrLDb0onnI0U4A1rm8VEmTh0zB3kDv5L7CSlJGlif7jlPjyedqtSiJoijKnFAB/CKUnRgG08S36h3ITIKJV39Msucw2YlhnPWTi4ZY/bUY8QmMicnMaFpsBKSJ9ZRWtREdZ2L3TxAb3svhbJYqp48nhzq40+Uh1+5CuPPO+G69tAEZD01+xjq5Q2R5NTbBL7qPUenOpcTpZftgOykjy3tqV6Br6kdMURRlLqjfrheh7MQQCIHVX4N76Y2kBpoI7/0ZujsfW/Fki9fqrwHTIDX1HlyPjaA5fGgOz/R14m2vgJGhsWEbadOkKz5BWpp8UGYQeWUzzhXXSxYgNZ3USDvZoTbucHkQwOHgIOsKK0hkMxwODrCpqIZKz5l/ACiKoihvDZWJ7SKUnRhCc3gRuhV7xVLSwydIDzTjWfn26aCrO7zo7nzSw624F25Fi41iKV902nUSJ14h6/CxV3eywOnlyeFO6lw5VCeG0CoWn1a2bSLArwZaSBtZkrYczM4jLM9EubF+PVdFomwfamdtYSUHx/tBwlUldefseSiKolyOVAv8IpSdGEb3Tk4FE0LgWfk2PKvfib1i2WnlLAVVxIZaebJ9P4lUFMsp87NhcspZ64KriBgZNuSV0hQOkK9bMI0s+ild7Ylshsd6m8ixOthSXMe1ZQ0sTEc5apgE86u4p34NuhBc4a9mf6CPhblFKuOaoijKHFMB/CIjTYNsZGQ6gANoFjuOiiVnTA+z+es4lorzy6YdvGC1Yck7mcAlExogHujicMkiyp0e8mwO+pJRyKbZKSxoeScHsD072IqJ5J55a7i9eim3LN7KrbrAabHzYtbgxvIGjrzjj5FALJtmS3HtnD8HRVGUy50K4BcZIxZEZlJYvEVvWtZaWM0RQyKC/fTpFvZkjeljsROv8GzhfMZySticX0FbLARAiYB9wkZTJgNAZ2Sco+ODXFc6n1J3DgC2wmq8Lh9bi6poj4Xoj02Qa3Oyd7SXIodHrTamKIpyDqgAfpGZHIFuYDnLqmAA6aFW0sNthIBuq5MNZopK3c5jI10MJqJIKXmifR/tuWVsq15FrTuH5vAYAFcJg3pvHk/0n6A3GuTJ3mYq3TlcV34yj7nQdHK3/Q7Xb34/foeb5wfbCCRj9MSCXFlcq5K2KIqinANz+ptWCHGTEKJFCNEmhPjsDMfvFUKMCiEOTX0+OrV/pRBilxCiUQhxRAjxnrms58UkOzEIQqC7889aZvB7H2fwe7/HgdAI0uljkcywyZ6PAH7U28zzI93sDQ2zypPH+oLJxC5HwqNowPJ0jLsrGih2ePhB+wFi2TR31azAqumnfYetsBqnz8/1ZQ30xUI80tOIy2Jjvb9qDu9eURRFec2cBXAhhA58FbgZWAy8VwixeIaiD0gpV059vjW1Lw7cI6VcAtwE/LcQIneu6noxyU4MT45At9hmPh4aJD14nNRAE3sHWqjIKcVtZLG6i3h7ST09iTBPdB6kfryLbZVLp89rjoyRZ7FTaqTILajmvgXrybU5uapkHnW+whm/C2CDv4oyl4/BeJi1hZW4zlIvRVEU5a01ly3w9UCblLJDSpkGfgLcPpsTpZQnpJStU/8eAEaAs/cZX0ayE0PonoKzHo+37ABg0O5leKCFFeULsa66jUxONbXuHK4sqGBeeJBrA21Y6q+YPq8jFsJvsZAvNKz5FZS4fPz1yut5R/XSs30VALqmcWvFYnJsDrYU1bwl96goiqK8ubmcB14O9J6y3QdsmKHcnUKIrcAJ4NNSylPPQQixHrAB7XNV0YuFNE2M8Ai28iVnLRNr2YHu9dNaUIcl0MkCbz56jh/GJ9Ofbi4oJ9l3ENPrRxTWIo0sE2M9jGdSrDFS6DY7Ft/kIDS7Prsfj+UFZSzMLcI2y/KKoijKb28uf+OemcYLXr/+5WPAj6WUKSHEx4HvAddMX0CIUuAHwIeklOYZXyDEx4CPAfj9frZv3/4WVf3CJJITOPsGSGm1ZFONZxaQJv7GF4iVr+NobjkNfbtpPdLCo8kR1mm50NgJUlLa8grJsjX0HTmO+8TjNMkM5BRSktFo9S7k+MuvnPubO4+i0egl/7Pzm1LP5uzUszk79WzOjbkM4H3AqStnVAADpxaQUo6dsvlN4F9f2xBC+IAngM9JKV+d6QuklN8AvgHQ0NAgt23b9pZU/EKV7G8iOJpL7tp1WF630AhAsvcovakJ0mtvRCRTLGr6Ob2yk/+d6OcnmpWvzbuRjZkoidQEeauvI98Xxsi1MlC9BTqPcOu172NL7crzcGfn1/bt27nUf3Z+U+rZnJ16Nmenns25MZfvwPcC84UQtUIIG3A38OipBaZa2K+5DWie2m8Dfg58X0r54BzW8aJiTAwhpUTzzDwCPd7yIhI4lFuFp7CGciPD4f7jCMCrWfng/if4ZvPLSECr24DZcxS9qJ5jJliExprXZWpTFEVRLlxzFsCllFngk8BTTAbmn0opG4UQ/yCEuG2q2B9OTRU7DPwhcO/U/ncDW4F7T5lidvk1DV8nOzGM7vChWewzHo+3vMhA9VpaMmnWF1Ziqb+CI4kw9e5cvly0gesLK/nndJa/X/5OMA1kMoy1bgMtEyPk212UTSVqURRFUS58czrqSEr5JPDk6/b9zSn//gvgL2Y474fAD+eybhejTHgY/SytbzMVJ9qxj51XfhRfMsLyZAgaruLYyCDbbA5cmoX/7tvHP472cX/Faj7YsZdFzhz0soV07X2SOm+BmgKmKIpyEVEpsy4S0jQxQoPTOdBfGO1h51g/Uk6OC0y076LRlc9wbgVXjXchj/6SIf98xmxuCsJD6P17MXb9gE8VVeLWLXwjOIyldi3BbJpwJsX8N5jrrSiKolx4VAC/QKWG2hh79v8R2nk/6dEuzHgIM5NE9xWRNLL8aqiTn/a18PDACQxpMnb8Rfbk11JVPI+aVBihWTjcfRCAwFgPI0e+jyhegP+mP+FuTw5PYqG/eAFNwWEAluedOShOURRFuXCpibsXGCMWInLoMRKd+9BsLjKj7SQ69kwmb5EmFq+fE9EQycanqY8H2VGzlkAyjtbfQiq/kmu9+QhpYq2/gqMtu9GlhcKJAbqsdja++/OgW7k3EeAHCL7R00SZa/K99wZ/5ZvUTFEURbmQqAB+gZCmQbzlJaJHfonMpnDN34xz3kZAkupvItG1D2Gxo3sLaeppxD7QxA19B6htf4XnK1ZjGgZL/VUUpCNkNQ3LvA009rZRFglgkSbttdtI+edhGz5BUTrGHSXLeKDzEGsLKrBpOivUCHRFUZSLigrgF4D0eD/hPT8lM9KBrXg+7mU3oDt90++3HVUrcFStmN5u6j5CZSKE675vszw6Rs7un3IkHmLr4qsxg70IiwPpyuNoJsV8m5OcqlUMGlZanv86i8w0Wk4Rn1h5Iw/86uu8MtpNuctHocN9Ph+BoiiK8mtSAfw8klISO/oU0aNPIyxWvGvvwF7aAIApJf95Yi/r80u4amqFLyEEw8kYwbFuVmUSjJYs5Jmxfn616t20RMdY6fFT3nMQLaeIrniIcCZFrr+SJYlxoqEIbf55rKpdgV5czzynj1srF/F4bzNV7jy1BKiiKMpFRv3WPo+M2DiRo7/CWlRH7tW/Ox28AcbSCQaTUZ4f7SVtGtP7j0fGkMF+vjf/aja+/FP+uvkVBlNRItkMX+88hIwG0HzFHB6bTHrndxew8OrfobR8A32FtZiVKxBOHwC/3wrxZ7sAACAASURBVLAJgGVqAJuiKMpFRwXw8ygbHADDwLXgSjSr47RjvYkwBpJQJsn+qZHiAE2j3YjoOI+5/dxRNp9nN9/F81e+h3eXN/DwQCvDqQRaTimHxgewaTqFDhdlLh81Vi+GadIZHZ++ls/m4H21q3h/3WWfI0dRFOWiowL4eZQJ9oEQ6O4zk7P0xMPoQlBsd7Ej0IspJSkjS8dAC12uPDTgsws2UO/JA+B3apZjSJPvChsip5jD4wNUe/Kw6Rb8DjfFFie5difNock/BhLZDI/0NrIiv5SNahlQRVGUi44K4OdRNjiA7slHzLAMZ3cszPd6mmiJBBlMxmgOj9ERmyA13sOe3CquKqigyO6aLl/l8nGLN58HsDLh8HIsNESp00uJ04NF09GEYGleKe3hMQzT5LHeJkxT8sF5a9QyoIqiKBchFcDPo0ywH31q7e1TZU2TxsgYkWyahwZOALA90EtzZIzhaJCA3c2dFQ1nnPc7DhsxBH/d+BJJI0uuzUmp82R+82V5xaRNg0d6G+mIjPGO6qWUqvzniqIoFyXV9DpPzFQMIxbEXrH0jGNDqRgDySgAaWnSFg0igMHQIF2aFR9wnb/6jPMWpsJssdt5pLcJAK/FTqnLO318vs+Px2qjKTjMOn8lm1TXuaIoykVLtcDPk0xoEMwslpwzR4D3xMOMJiLo0uTdZpLnRruJGRnGR7to9BbztvwSHK/r9pZSQnScjxdNBnaPxUau3Um562QL2zKVsKXM5eM9tSsRQsztTSqKoihzRrXAz5PseD9IsPiKzjjWG48QCg1SlwjziSM/47FNv0dLJIg9HCBt8/Cu+vVnXjAZBiPD5pJ5bEgmEXJyje+SqSljr3lXzTIMKbGr996KoigXNfVb/DzJhgbQHG6iQsMr5Wmt4a6ew4yZJtvcOZQsvJoPdO/mm2ygTEJtNsWq3DPfm5uRwOSI9rxSflS9kqf7W+iNhcixnT49zaLp6n+6oijKJUB1oZ8nmWA/SXch/3piDy8Geqf3xxMROhqfI2x1snT+Fdjv+hc+opl4sykG7B7udHtm7PqW4WHQLAhPIQ7dQjCVoMzlU93kiqIolygVwM8DaWTJTgwx6s4jmk3zwmgvSSMLQNMzXyEwlXltcW4xwmKj+P1f4mNjbdiNDO+ct27ma0YCaN4ChNWOlJKxVOy0EeiKoijKpUUF8PMgGx5BZtMM2yZHiIcySXaND5AaaKLtwCMECmsBWOQtAEA4fXzi9r/k5eIyyivPHLUOIKOB6SlpkUyKpJGlzOWbsayiKIpy8VOvQ8+DTLAfpGTYYidHQoHVyfbRXuoOP8yIu4BIThnF2TQFNuf0OXpOMf4175zezra/ikzH0MqWoHkKkfEQom5ycNtoMoqGOG0EuqIoinJpUQH8PMgG+xEWK/2mgd/m4sqCcr7bfYxX+5oZLVvCmJGZbn3PRGZSGO27EEIn230IaffQIyUl7nzypWQwHsGiaRQ5PefwrhRFUZRzSQXw8yAT7CfjKSSQSjAvP49ih5tamWGfbiedV8lIMsY7S+ef9XxzrBukxL71Q5CMsbf1VZ4zQAsM4oxGMJEUOtxqqpiiKMolTP2GP8eklBjBAcZzy8lKSandDcCmsU5+pOkEPEUY4wNntMCllJhIdKFhjrSjObzoRfUgBEdTaeqtdjYV1dAbC9EbC7F4hqlmiqIoyqVDBfBzzIyHMFJRhhw+pCEpdkwG8MLO3dQALZoOwGLf6QG8MRLg6ZFu7qtaijPQibV8KULT6IuGGEtGuaViIev9VWzkzBSriqIoyqVHjUI/xzLBAZAmI1YXLosVt8WKNA2M9le5obCCApsLh6ZT87oR5EcmApjS5MW+Rkgn0EsnFzPZN9aHz+pgZX7Z+bgdRVEU5TxRAXyOmdJk90g36al53plgHyAYROC3Ti4HavY3QjKCp/4KehJhFnoL0MXJ/zUJI0tfIkKhzcXxkS6GNSt68Tzi2TQtE6Os81eqJUEVRVEuMyqAz7HOyDg/6TzEzpEupGmQ7D6M9BQwlE5SMtV9brTtBECr20BzZJxF3vzTrtEaHUcA76tchC8VYYerAGwuDo8PIECtKqYoinIZUgF8jnVHg6SMLLtGuom37yYb7CVWt5G0aVDimGyBG2070UoXMWhxMJFNsfh1A9iaI+MUO1xUWqxsSUcYdObSGg5wINDPghw/RU7vTF+tKIqiXMJUAJ9jvbEQmhAMRoM0Hv4llrxyRtyFmEhK7G5kOoHZfQC9/gqaI2MA0wHc6DtKfLyfnkSEpT4/mdFOVmgaFUU1PNbTRDiTUq1vRVGUy5QK4HOsJxpkgc+PLdTP/mgI16JrGEhMYNN0cqx2jK59YGTQ6jdxNDwKQIM3H5mKkTn2FK17H0RGx1jmKyQ93IrNncttC64gYxoU2F0szSs5z3eoKIqinA9q5NMcSA21YSZCZEsWMp5KsMTtwxXo4qirgIEXv0lL52H8C6+CmuWYbTtBt6JVr+bJvU+yJrcYj8WGEehEaDptjjx8I634hypJBDpx1qxhUW4xW0vqqHDnYpmadqYoiqJcXlQAf4tJI0vfV96FzKYYWX0HGUcBRZEByswkB/KWsO/4cwQ0K4v2/YzE/p+BkUarXkVTKkFrLMjnF18JgBnoxrC66alazbqJPuJNzwMSW9lihBC8q3bF+b1RRVEU5bxSXehvsUTvEVIDzWRDgwzY3MjRDnJ7D1FYvZJyTy57sgaZqpWU3Php0AQyNIBev5mHB05gExpvK5kHgBzvpSe3DFNobLjivbiXXo8lpwR7ydlTrCqKoiiXD9UCf4tF9v8CjDRmIk2gaD75uZU4bRb0imWs6D3KLwVIdz5lDVtxLrkW48RLyNoNPLrrYa7xV5FjtSNTMWQ8RKu/gRy7g7qcQrTVtyFXvR0hxPm+RUVRFOUCoFrgb6HsxDCJ9lcBkED3SAel3kIsNWsQFhvzQwPYjSy6x0+BzYHQrVgWXcPLkTEC6QR3lC8AwBzvpU/otOo2luWVoU0ldVHBW1EURXmNCuBvoXjrToyJIbA6iFjsRCaGKHWcXNJTGz7BusgQi4vrTsu09vBAK3lWO9sKKwFoGjzBw5qdsrxSbpwK6oqiKIpyKtWF/hYxMykSnXsx4iGcNWvoik9gRgKUnbImtznUwhKHG624ZnpfJJvm6ZEu3lPegFVovDo+wIvjQ8zPK+XjS7bitFjPw90oiqIoFzrVAn+LJLsPYsSCZIMDOKpXESheiB4ZpdDmnC4TGengnvpr2LD9h3ypbT/xbIZfDnWQMg3eXlLPk0Md7BjqYJmZ4r7561XwVhRFUc5KtcDfAlJK4q07kUYapIGjejUB0UjB4NPo6Tg4PKRi4/xR5TqOWpxszi3mi+37ub+vGbdupdrp40h4hFAmxfUOJ+t1DVeJ6jpXFEVRzk61wN8CmWA/mbFuYHKQmb1qJYPuQopTUczB45hS8ieHn+OV/Fr+qaCEH6y9lYfW30a5w0NnfIJyp4eMNPlY7Qq2ZqJYHB4sOSrDmqIoinJ2qgX+FsiMdIBpkA0OYMkrZ8LhI+nOpygdwxho4gvJNI9FgvxRx0vcfcV/ALAmr4QvLrua/+k4wIbcEu6tWUaezUFwvBdr8TyEpv62UhRFUc5uTqOEEOImIUSLEKJNCPHZGY7fK4QYFUIcmvp89JRjHxJCtE59PjSX9fxtZcZ70Rw+Un1HcVSvoi8RxrA68VusbB/p4rs9x/hQJsJHAycQ3iIA4tkMjw62scrM8FG3l1xdx0xGMWLj2Irqz/MdKYqiKBe6OWuBCyF04KvA9UAfsFcI8aiUsul1RR+QUn7ydefmA38LrGVySvX+qXODc1Xf30ZmvA9hc5IN9uHY+mF64hEsmkahv5Y/EjbKHR7+uP059JIGhBBIKXl0sA0yCd4+3EwqcIK0pqN78kHTsRWrAK4oiqK8sblsga8H2qSUHVLKNPAT4PZZnnsj8IyUcnwqaD8D3DRH9fytmJkU2fAwZjIMQKxsKQcnhilxuDlQtpSDznxWeAvYlYwzUbIQgFfHB+lNRLjV4aTIYiF3y724GrYipcTiK1bvvxVFUZQ3NZfvwMuB3lO2+4ANM5S7UwixFTgBfFpK2XuWc8tff6IQ4mPAxwD8fj/bt29/a2r+a9AiQzgGRxDJCbIWJ1/uC5ARGv+/vTsPz6q6Fz3+/b1D3jfznDCTMAZECJOCio1WafUqVI/tldJBvae9nur12N5eD7T3qdjjY1GstrfVWo/DsT0ULCpqOajVVg4OiBJkCEKAQAJhyDxP77TuH3snvAkJYchLCPl9nud92Hvttddeez0r/N49vGvNjx3OYyaOhEAzKeWl7IhJYZMjhZTNH1IebGFMVDyeqn0crm+i8GAdSDIkzgMTgo0b+7yejY2N/dI+A4G2Tc+0bXqmbdMzbZvzI5IBvLtxP02X9b8Aq4wxbSJyN/AScO1p7osx5lngWYCJEyeavLy8c6rw2Wja8wH1NUlUHCrm9Ql5eDKSuGvEJI60NpF/zPCVyhLGRcdxY2k+ZfPvYYszmjRHCveOnUHLxiLcY+aS/KVrIl7PDRs20B/tMxBo2/RM26Zn2jY907Y5PyIZwEuBkWHrI4Cj4RmMMVVhq/8GPBq2b16XfTf0eQ37QKCmlNaoGNb4AjSmD+P24RPJ8Mbyky8+IMHpZlRbE6OPFRAbCnD9+LnMd3sJYZCAj8amamImzuvvU1BKKTUARfIZ+GfAeBHJFpEo4HbgzfAMIjI0bHUBsNtefgeYLyLJIpIMzLfTLji+qsN8HjRUi4OFGVkMi45nd0MV71Uc4sYhY3HHJjOmsQJ3WjaOqGhEBKc4CNQeB8CdltW/J6CUUmpAitgVuDEmICL3YgVeJ/CCMWaXiPwc2GKMeRO4T0QWAAGgGrjD3rdaRP4V60sAwM+NMdWRquvZMgEfgboytrY0M7yljuwxswD4TdFW4pxuJsQlYxJSSfU34xk2qdO+gepDiDMKd/KI/qi6UkqpAS6iA7kYY9YD67uk/SxseSmwtId9XwBeiGT9zlWg7jjFQR81zbXMDvmRtGw+rTnG+rKD/CA7lyp/K/NSRyFAVJcA7q85gitpKI4ob/9UXiml1ICmw32dA3/VYbYGwVtzlHHDcwhheGj3xwz1xnJt+kgwhqlZ00GE6KyZHfsZYwjUHcOdNrofa6+UUmog06FUz0FNZTGFAT+X1pTgmf59Xjmyl10NVfx66rWUNNeT6olmbNZ0Ag9+ijtpWMd+oeYajK9NA7hSSqmzplfg5yC/7CABXwuTG8tpGj2TFfs+Y1ZSJjdkZFPcXM+UhDREpFPwBvBXHwGHgyh9gU0ppdRZ0gB+lkJ+H/n1VQxrrCAlLpWnq8uo8rXwYM4VlLTUEzSGSxPSut3XX30YpycOZ1z325VSSqneaAA/S/uOFVIZ9HPJ0QKOjruKF0sK+PrwiUzxePniwGck+JrIik3sdt9A7VHcaaN1xjGllFJnTZ+BnyZjDMbXjMMTC8Cmw7uIam1kbF0pbwzJwd/QwPfqDtO2YQsluJnoicHRzYByJugn2FhJ9JjLzvcpKKUGOb/fT2lpKa2trRE9TmJiIrt37+4940XM6/UyYsQI3G53xI6hAfw0NX/xPg3b1+FKHYWMmMrOsoPkNFXiMoZ9ISEWw2iXi/pLrqe1/DCjq4oINlXjikvtVI6/9jgYgzs9u5/ORCk1WJWWlhIfH09WVhYi3Y1Y3TcaGhqIj4+PWPkXOmMMVVVVlJaWkp0duf/r9R7uaQg0VtFY8DbOuDRCrY3kb3mNltqjTKg5BOljKGyuY4LbQ+xX/pnSITm4kocx2uXGd3zvyWXVlILDRVTKyG6OpJRSkdPa2kpqampEg7cCESE1NTXidzo0gPfCGEPDlrWYYICE2beRfPVdFE28lviUkWSU78OROZ69BnLSRwFwoLGKIfFppKWOwFe2/6TyfOVFuBKH4vDEnO9TUUopDd7nyfloZw3gvWg9vIPWwzuIycnD4Y2jMeCj0NfKpNgExN9KhTuGWoRJmWMJGcPhxjrGJ6TjGT6ZQO0RQv4T38CCTTUEqg4Rkz3zFEdUSimleqcB/BRCvlYa8l/HlZCOd/QMAHbWVeILBck5Zr2gsT/W+ilYTmIGZS0N+EMBJiSm4Rk2GQz4Kg52lNdSshVxe/HqC2xKqUHK6XSSm5vLtGnTmDFjBh9//PEp89fW1vL000/3Wm5eXh5btmw5KX3Dhg0kJiaSm5vL1KlTue666ygvL++UZ+HChcydO7dT2rJly3j88cd7rP8ll1zCtGnTeOKJJwiFQr3WLxI0gJ9C4853CDZUEjv1xo6ffH1eW0aqJ5rEok2QkMm+xCEA5CSmU1RfhUucjI1PxZ0yEmd0Iv7j+wAwoSC+0p14R0zBGT14X+5QSg1u0dHRbNu2je3bt/OLX/yCpUu7nQ6jw+kG8FOZN28e27ZtY8eOHcyePZunnnqqU/lbt26ltraWgwcPnqKUzvXftWsX7777LuvXr+ehhx46p/qdLX0LvQe+6lKa92zAkzUdd5I162mtr5UDTbXMcUdhij9DRs9gf2w66cFKUr2xFDfWMCIugVi3B4CoYTm0HtqOMQZf2X5Cvla8Y+f052kppRQAx1feT+uhbX1apndULkMW/+q089fX15OcnAxAY2MjCxcupKamBr/fz8MPP8zChQtZsmQJRUVF5Obmcv3117NixQoee+wx/vjHP+JwOLjhhhtYvnw5AGvWrOEHP/gBtbW1PP/888ybN6/T8YwxNDQ0MG7cuI60V199lZtvvpnMzExWr17d6xeKcBkZGTz77LPMnj2bZcuWnff3CzSAd8OEQtR/ugZxRRGbk9eRvr2uHL8JMaHw74DgmpjHXr+PnMQMAqEQR1vquXbI2I78nuGTadn/CcG647QW5+NKSMczZML5PyGllLpAtLS0kJubS2trK8eOHePvf/87YP1ueu3atSQkJFBZWcmcOXNYsGABy5cvp6CggG3brC8bb731Fq+//jqbN28mJiaG6uoTM00HAgE+/fTTjqvi9957D4APPviA3NxcqqqqiI2N5ZFHHunYZ9WqVTz44INkZmZy2223nVEABxgzZgyhUIjy8nIyMzPPtXnOiAbwbjTv/RB/+QHiZ96Cw31ius/Pa8sZGuUl+rM1SOoonOOvoHDPZ3xr7EyONNUSDIWYkJjekd8zZCLiiqKlOB9/VQnx0xfo6GtKqQvCmVwp96X2W9AAmzZt4jvf+Q4FBQUYY/jJT37Cxo0bcTgcHDlyhLKyspP2f++997jzzjuJibF+yZOSktKx7dZbbwVg5syZFBcXd6TPmzePdevWAfDoo4/ywAMP8Mwzz1BWVsb+/fu56qqrEBFcLhcFBQVMmTLljM7JGHNG+fuKRpMugk21NG5fjzs9m6ihEzvSy9uaOdTSwKTKg9BUhQybRGl0Iq3BAJMSMzjQWI3H6SI7/sTALQ5PDO60LFoPbUMcbh19TSmlwsydO5fKykoqKipYuXIlFRUV5Ofns23bNjIzM7v9HbUxpsdb1R6P9fjS6XQSCAS6zbNgwQI2btwIwMsvv0xNTQ3Z2dlkZWVRXFzM6tWrz+gcDhw4gNPpJCMj44z26wsawMMYY6jPfw0TaCNu2o2dOsmOugpCxpC9/U2IS8ORNpq9dvNNTEynpLGG0bFJeJydb2p4h09GxIFn5KU4Y7ofG10ppQajPXv2EAwGSU1Npa6ujoyMDNxuN++//z4lJSUAxMfH09DQ0LHP/PnzeeGFF2hubgbodAv9dHz44YeMHWs96ly1ahVvv/02xcXFFBcXk5+ff0YBvKKigrvvvpt77723X35fr7fQbSFfK82FG2kt2UbMpGtwRid02v5FfSVDAy1E7/8Ix9SbcHgTKGxpAiArLpl3jhQyY8SIk8r1jJyKY9d7xIy/8rych1JKXcjan4GDddH00ksv4XQ6Wbx4MTfffDOzZs0iNzeXnJwcAFJTU7nyyiuZMmUKN9xwAytWrGDbtm3MmjWLqKgobrzxxk7PtLvT/gzcGENiYiLPPfccxcXFHDp0iDlzTrxYnJ2dTUJCAps3bwbg4Ycf5le/OvGoobS0tKP+fr8fl8vFt7/9bX70ox/1dTOdlkEfwIPNdTQVfkDLvo8ItTYQlTmB6OzZnfLU+9s41FzPZQc/BYcLx5BxOJKGUVhfwejYZCpam8DAhIT0k8p3JaSTcevPEVfkBrRXSqmBIhgMdpuelpbGpk2but32pz/9qdP6kiVLWLJkSae0DRs2dCqr/Rl4Xl4edXV13ZZ75MiRk9K2bt0KwOWXX86yZctOu/79YVAH8JCvhar1Kwi21BM1NIfocXNxJ578FuHuhmr8AR+jt6/DMfnL4PfhSBzKnqMHyUlKZ1v1EVK9MYyOS+72OBq8lVJK9bVB/QzcV36AYEsdCZffTsLMr3UbvAF21ZaRuP9jkhuO45723yAUwJ80hAMNVWTHp3KgoZpZaaNw6hvmSimlzpNBHXF8ZfsRhwt3as8zgzVVlrBrw7+Rvftd3HO+CcnDweHgoMtL0BiinS4cCJfbk5kopZRS58PgDuAVB3AlDkUczm63Nxb8lY+eXkSgqYbxX74Hz8IHMfVliDuaQp8PgBa/n3EJaaR5Y89n1ZVSSg1ygzaAh3wtBKpLcaedfOVsQiGq3volx567k5L0sURd9g1Gz7jZ2lZXjiNpKIX1FbhEENGrb6WUUuffoH2JzVdxEBP04UrL7pQeamvi+Mr7adqxntjZX+fotFsZE52AUxwYYzCNlTSkzuDz6qNkeuNJiPIyNWVYP52FUkqpwWrQXoH7yooQcXZMVALgry7l8K8X0rTzbdK+9iD+rz1EbTDAhLhkChuqWfzpG1wZcDP9wC4+Li8mMcrL9JThJw3eopRSqnu9TSf65JNP4vV6O/30a8OGDdx0000nlZWXl8fEiROZOnUqOTk53HvvvdTW1kb8HC4UgzeAVxThTByC2MG35eAWip68mZLGOhL/8d9Jzvs+uxuqQCA7NpE/HN5Ffl0lV0uQn064jAenXc/VmWOYo7fPlVLqtPU2neiqVauYPXs2a9euPa3yVq5cyY4dO9ixYwcej4eFCxdGotoXpEF56RjytRKoOozXHrClPn8t5av+N5uHT+PzOd/CFXSQsPtjAqEQw7xxeBxO3i0rJtvlYrK/FYcnjjp/K1nxyYzq4bffSil1Ibt/8xtsqz55IJNzkZsynF9dfvoBNHw6UYCioiIaGxtZsWIFjzzyCHfcccdplxUVFcVjjz3GuHHj2L59O9OmTTuTqg9IgzKA+6pKMEEf7vQsqt5+guq3f0n02DmUXXMfmS4vk+JTqWhrotLfwuzkIexqqKLc18I0t5uh8anEJqYjIlyWNrJfxr9VSqmBqqfpRMG6+l60aBHz5s2jsLCQ8vLyM5okxOl0Mm3aNPbs2aMB/GLlL9sH4iBQc4Tqt39J/Kx/IOrWhynbn8+8+FQuSxnSKf+v9+cDcIXxsXjENBLHz+6uWKWUGjDO5Eq5L/U0naiIsHr1atauXYvD4eDWW29lzZo13HPPPWdUfn9N7dkfBmUAbysrwpU4lLoPXsQRk0TG15fzWVMdARNiXFxSRz4TCgHwbnkJmVHRTPTX4k4+ecISpZRSZy58OtHjx4+zb98+rr/+egB8Ph9jxow5owAeDAbZuXMnkyZNilSVLyiD7iW2kL+NQNUhJCqaxh1vkTh3MQ5PDHsbq4l3RZHstuaTDdWX49vwDIff/Q0FDZWMDrYxUgRXigZwpZTqC+HTia5atYply5Z1TO159OhRjhw50jGtaG/8fj9Lly5l5MiRTJ06NcI1vzAMuitwf6X1/Lul6BMQB4nz7iBoQuxtqCErJhERIdRQif+zP+PwxvNh6lA4XsxYgZHxabjCfnamlFLqzPQ0nejq1at56623OuW95ZZbWL16NZdffjl/+9vfGBE2ZfOaNWsAWLx4MR6Ph7a2Nq677jreeOON83cy/WzQBXBf2T5MMEDj538hLvcm3EnDKGmqoyHgI9bp4pWirdx0cBNObxzevO/x/tZ3SI6KJvfSaxh16TX60ppSSp2DnqbjPHjw4ElpTzzxRMdyS0vLSdvDpxAdjAZdAG87uht/dSnG10Ry3vcAKGysBuB3+/P5pL6C3S4PP7v6Lto8cWw8foBJSZlkx6dq8FZKKXXBGFQBPNhYjb/6EK0HP8WbPRvvKOs2zt6GGkIYPqmvYIwYng+EiCneycy0EbSFgoyMSWJMfGo/114ppZQ6YVAF8NbSAnzH9xGsLyf9Hx4GoDngp6Slnu21ZcRieC1rCivEw292f8SwmAS8Thej4pLI0gFblFIXAWOM3k08D87Hz9kGXwCvOIAraRhxl34VgKKmWqraWthcc5w78JOclcvy9Gz8oSB/Lt5ObsowUjwxpHhi+rn2Sil1brxeL1VVVaSm6iPBSDLGUFVVhdfrjehxBk0AD7U14TteiL+imMS5izrmAC9srKagvhIHcEd0DM60LESEx2ffxOSkTI4315Mdl6KdXSk14I0YMYLS0lIqKioiepzW1taIB68Lndfr7fTWfCQMmgDednQ3/soSCPqIyckDrG9Jn9eW80VDFTcToC5tAs/t+4xvZE8jMcrL4rHT+fWuDxmToM+/lVIDn9vtJjs7u/eM52jDhg1Mnz494scZ7CI6kIuIfFVECkVkv4gsOUW+20TEiMgse90tIi+JyE4R2S0iS3va93S1le4kUH8cxEnMuCsA2FFfwX9VHMZnQtyFj53RSdT6WviP/fk0+tsobapDgKxYff6tlFLqwhKxAC4iTuAp4AZgMrBIRCZ3ky8euA/YHJb8dcBjjLkUmAn8TxHJOtu6mICPtqN7CFQfJXrMZTi8cbQGA7x2ZB8FDZVc7XYzJiaRYw43U5OHEsKwsmgre+sr8DhdDI9NxLEAOwAAC1pJREFUPNtDK6WUUhERySvwy4D9xpgDxhgfsBrobvT8fwUeA1rD0gwQKyIuIBrwAfVnW5G24/sINFQQqDlMTM6XAPhrWTFfNFTSHAzw3/2NlGWMxwDzMrP53oTLaQn6+bzqCKNik3DZz8uVUkqpC0Ukn4EPBw6HrZcCl4dnEJHpwEhjzDoR+XHYplewgv0xIAb4oTGmuusBROT7wPft1TYRKei1Vn9YDizvlBQ+VP5vu9nlvl4LHRDSgMr+rsQFStumZ9o2PdO26Zm2Tc8m9lVBkQzg3b223fHDOBFxAE8Cd3ST7zIgCAwDkoEPROQ9Y8yBToUZ8yzwrF3eFmPMrL6p+sVH26dn2jY907bpmbZNz7RteiYiW/qqrEgG8FJgZNj6COBo2Ho8MAXYYP9EawjwpogsAL4JvG2M8QPlIvIRMAvoFMCVUkqpwSqSz8A/A8aLSLaIRAG3A2+2bzTG1Blj0owxWcaYLOATYIExZgtwCLhWLLHAHGBPBOuqlFJKDSgRC+DGmABwL/AOsBv4szFml4j83L7KPpWngDigAOuLwIvGmB297PPsudb5Iqft0zNtm55p2/RM26Zn2jY967O2kfMxXqtSSiml+lZEB3JRSimlVGRoAFdKKaUGoAETwEVkpIi8bw+tuktE/tlOTxGRd0Vkn/1vsp0uIvL/7GFcd4jIjP49g8gTEaeIfC4i6+z1bBHZbLfNy/bLhIiIx17fb2/P6s96R5qIJInIKyKyx+4/c7XfWETkh/bfU4GIrBIR72DtNyLygoiUh48ncTb9RES+a+ffJyLf7Y9z6Ws9tM0K+29qh4isFZGksG1L7bYpFJGvhKWf1vDaA0137RO27cdiDRWeZq/3Xd8xxgyIDzAUmGEvxwN7sYZofQxYYqcvAR61l28E3sL6PfocYHN/n8N5aKMfAX8C1tnrfwZut5efAf7JXv4B8Iy9fDvwcn/XPcLt8hLwj/ZyFJCk/caANdjSQSA6rL/cMVj7DXA1MAMoCEs7o34CpGD93DUFawyLA0Byf59bhNpmPuCylx8Na5vJwHbAA2QDRYDT/hQBY+y/w+3A5P4+t0i1j50+EutF7hIgra/7Tr+f+Dk02BvA9UAhMNROGwoU2su/BxaF5e/IdzF+sH5n/zfgWmCd3Tkqw/7A5gLv2MvvAHPtZZedT/r7HCLULgl2kJIu6YO+33BitMQUux+sA74ymPsNkNUlSJ1RPwEWAb8PS++UbyB/urZNl223ACvt5aXA0rBt79j9qKMvdZdvoH+6ax+sUUWnAcWcCOB91ncGzC30cPatu+lYE6BkGmOOAdj/ZtjZuhvKdfj5q+V59yvgASBkr6cCtcb6OR90Pv+OtrG319n5L0ZjgArgRfvxwnP22AKDvt8YY44Aj2ONu3AMqx/ko/0m3Jn2k0HTf7q4C+uqErRtALB/Ln3EGLO9y6Y+a58BF8BFJA54FbjfGHOqCU5OOZTrxUREbgLKjTH54cndZDWnse1i48K6tfU7Y8x0oAnrVmhPBk3b2M9zF2Ld5hwGxGLNHtjVYOw3vempLQZdG4nIT4EAsLI9qZtsg6ptRCQG+Cnws+42d5N2Vu0zoAK4iLixgvdKY8xrdnKZiAy1tw8Fyu303oZyvZhcCSwQkWKsWd+uxboiTxJrRjfofP4dbWNvTwROmizmIlEKlBpj2qerfQUroGu/geuAg8aYCmMNW/wacAXab8KdaT8ZTP0H+0Wrm4DFxr7vi7YNwFisL8bb7f+XRwBbRWQIfdg+AyaAi4gAzwO7jTFPhG16E2h/W++7WM/G29O/Y7/xNweoa78VdrExxiw1xoww1pC0twN/N8YsBt4HbrOzdW2b9ja7zc5/UX4TNsYcBw6LSPsMQF8GvkD7DVi3zueISIz999XeNoO+34Q5037yDjBfRJLtOxzz7bSLjoh8FfgXrCGwm8M2vQncbv9qIRsYD3xKL8NrX0yMMTuNMRnmxFDhpVgvYR+nL/tOfz/4P4MXBK7Cup2wA9hmf27Eegb3N2Cf/W+KnV+whmQtAnYCs/r7HM5TO+Vx4i30MVh/OPuBNYDHTvfa6/vt7WP6u94RbpNcYIvdd17HesNT+411vg9hzTNQAPwR683hQdlvgFVY7wL47f9w/8fZ9BOs58H77c+d/X1eEWyb/VjPbNv/P34mLP9P7bYpBG4IS78R6xdERcBP+/u8Itk+XbYXc+Iltj7rOzqUqlJKKTUADZhb6EoppZQ6QQO4UkopNQBpAFdKKaUGIA3gSiml1ACkAVwppZQagDSAK9WHROQWe+ahnAiVvz581qez2D9P7Nnqesm3QURm9ZLnfnvEqT5j1++KM8g/TERe6cs6KDVQaABXqm8tAj7EGqSizxljbjTG1Ian2QNC9Mff8v1AnwZwrHEMTjuAG2OOGmNu6z2nUhcfDeBK9RF7nP4rsQa5uL3LtgdEZKeIbBeR5XbaTHt9kz23coGdfoeI/DZs33UikmcvF4tImohkiTW3+dPAVmCkiMy3y9oqImvs+rTPwbxHRD4Ebu2h7tEistqen/hlIDps2+9EZItY84Y/ZKfdhzV++vsi8n5P+ez05SLyhV3243Zauoi8KiKf2Z8rxZqk6G7ghyKyTUTmdanjl+z0bWJNTBNvt0N7uz0Xtr1CRB600/+PfYwd4fVSasDr7xFs9KOfi+UDfAt43l7+mBPz199gr8fY6+2jee0AvmQvr8CeihBrTu7fhpW7Dsizl4uBNKypC0PAHDs9DdgIxNrr/4I1kYIXa7Ss8VgjQP0Ze6S+LnX/EfCCvTwVa3KKWV3q6wQ2AFPD6xJWxkn5sKYqLYSOQaOS7H//BFxlL4/CGiIZYBnw4x7a9y/AlfZyHNZENVmcPIXjaKzR5UZjDUf5rH3uDrstr+7vvqIf/fTFR6/Aleo7i7Amk8H+d5G9fB3worHHizbGVItIIlYw+y87zx/P4nglxphP7OU5wGTgIxHZhjVu92ggB2vCkn3GGAP8Rw9lXd2+zRizA+vLRbtviMhW4HPgEvs43ekuXz3QCjwnIrcC7WNmXwf81q7rm0CCiMT3cr4fAU/YV/9J5sSUpx1EpH2413uNMSVYAXy+XaetdnuM7+U4Sg0Irt6zKKV6IyKpWLPATRERg3UVakTkAayrv65jFneX1i5A58db3h7yNXUp711jzKLwDCKSe4rjdHVSPnsyih8Ds40xNSLy793Vp6d8xpiAiFyGNVHK7cC9WO3kAOYaY1q6lNNz5YxZLiL/iTWe9icich3Wl4NwzwCvGWPeay8S+IUx5ve9nbxSA41egSvVN24D/mCMGW2sGYhGAgexJuH5K3BX+xvbIpJirBfR6kTkKnv/xWFlFQO5IuIQkZHAZadx/E+AK0VknH2MGBGZgHUrOVtExtr5FvWw/8b2OojIFKzb3wAJWF8U6kQkk87zhTcA8afKZz+HTzTGrMd66S3Xzv9XrGCOnS+3mzI7EZGxxprl6VGsyWlyumy/B4g3xiwPS34Hq+3b3wcYLiIZPbSBUgOKXoEr1TcWAcu7pL0KfNMY8092gNoiIj5gPfAT4E7gBRFppvO0gR9hBf+dWLOEbe3t4MaYChG5A1glIh47+f8aY/aKyPeB/xSRSqw35Kd0U8TvgBdFpH22v0/tcreLyOfALuCAXbd2zwJvicgxY8w1PeSLB96wb20L8EM7/T7gKft4LqwvEHdjPed+RUQWAv/LGPNB2PHuF5FrgCDWtKdvAUPDtv8Y8Nu35cGaHesZEZkEbLKv7hux3lUoR6kBTmcjU+oCYL+Bvc4Y011wVUqpk+gtdKWUUmoA0itwpZRSagDSK3CllFJqANIArpRSSg1AGsCVUkqpAUgDuFJKKTUAaQBXSimlBqD/D5NzA3mGfsxzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for show_b in (10,):\n",
    "    filtered = {}\n",
    "\n",
    "    def filter_exps(name, store):\n",
    "        vip_args = rl.get_vip_args(store)\n",
    "        if vip_args.nap < 1200:\n",
    "            return False\n",
    "\n",
    "        if vip_args.ds not in (DatasetEnum.cinic10,):\n",
    "            return False\n",
    "\n",
    "    #     if vip_args.nap < 300:\n",
    "    #         return False\n",
    "\n",
    "        if (vip_args.am, vip_args.af) not in ((AcquisitionMethod.multibald,\n",
    "                                               AcquisitionFunction.bald),\n",
    "                                              (AcquisitionMethod.independent,\n",
    "                                               AcquisitionFunction.bald),\n",
    "                                              (AcquisitionMethod.independent,\n",
    "                                               AcquisitionFunction.random)):\n",
    "            return False\n",
    "\n",
    "        return True\n",
    "\n",
    "    filtered.update(rl.filter_dict(stores_cinic10, kv=filter_exps))\n",
    "\n",
    "    pp.pprint(rl.diff_args(filtered))\n",
    "\n",
    "    def key2text(name, store):\n",
    "\n",
    "        vip_args = rl.get_vip_args(store)\n",
    "        am, af = vip_args.am, vip_args.af\n",
    "        \n",
    "\n",
    "        if (am, af) == (AcquisitionMethod.independent,\n",
    "                        AcquisitionFunction.random):\n",
    "            return 'Random'\n",
    "        elif (am, af) == (AcquisitionMethod.independent,\n",
    "                          AcquisitionFunction.bald):\n",
    "            return f'BALD'\n",
    "        elif (am, af) == (AcquisitionMethod.multibald,\n",
    "                          AcquisitionFunction.bald):\n",
    "            return 'BatchBALD'\n",
    "        else:\n",
    "            raise ValueError(vip_args)\n",
    "\n",
    "    #grouped_by = rl.groupby_dict(filtered, key_kv=rl.get_diff_args_key2text(filtered, ('tag',)))\n",
    "    grouped_by = rl.groupby_dict(filtered, key_kv=key2text)\n",
    "\n",
    "    pp.pprint(\n",
    "        rl.map_dict(grouped_by, v=lambda stores: rl.get_stores_info(stores)))\n",
    "\n",
    "    grouped_by = rl.map_dict(\n",
    "        grouped_by,\n",
    "        v=lambda stores: rl.aggregate_accuracies(stores, thresholds=(50, 55, 58, 59)))\n",
    "\n",
    "    sorted_dict = rl.sort_dict(grouped_by, v=rl.get_threshold_quantiles_key)\n",
    "\n",
    "    plt.figure(figsize=(7, 5))\n",
    "    #plt.title(\"RMNIST\")\n",
    "    alp.plot_aggregated_groups(sorted_dict,\n",
    "                               show_num_trials=False,\n",
    "                               show_quantiles=False,\n",
    "                               show_thresholds=False)\n",
    "    plt.axis([200, 1400, 0.48, 0.60])\n",
    "    acc_label_axes()\n",
    "    plt.grid(True)\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.title('CINIC-10')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    output_path = blackhc.notebook.original_dir + '/CINIC10_poster.pdf'\n",
    "    alp.plot_save(output_path)\n",
    "    plt.show()"
   ]
  },
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